{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"./TMDB/train.csv\",index_col=\"id\")\n",
    "test = pd.read_csv(\"./TMDB/test.csv\",index_col=\"id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.concat([train,test], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7398 entries, 1 to 7398\n",
      "Data columns (total 22 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   belongs_to_collection  1481 non-null   object \n",
      " 1   budget                 7398 non-null   int64  \n",
      " 2   genres                 7375 non-null   object \n",
      " 3   homepage               2366 non-null   object \n",
      " 4   imdb_id                7398 non-null   object \n",
      " 5   original_language      7398 non-null   object \n",
      " 6   original_title         7398 non-null   object \n",
      " 7   overview               7376 non-null   object \n",
      " 8   popularity             7398 non-null   float64\n",
      " 9   poster_path            7396 non-null   object \n",
      " 10  production_companies   6984 non-null   object \n",
      " 11  production_countries   7241 non-null   object \n",
      " 12  release_date           7397 non-null   object \n",
      " 13  runtime                7392 non-null   float64\n",
      " 14  spoken_languages       7336 non-null   object \n",
      " 15  status                 7396 non-null   object \n",
      " 16  tagline                5938 non-null   object \n",
      " 17  title                  7395 non-null   object \n",
      " 18  Keywords               6729 non-null   object \n",
      " 19  cast                   7372 non-null   object \n",
      " 20  crew                   7360 non-null   object \n",
      " 21  revenue                3000 non-null   float64\n",
      "dtypes: float64(3), int64(1), object(18)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4398, 21)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3000, 22)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "id:电影的唯一id\n",
    "\n",
    "belongs_to_collection:这个电影系列的系列名称（不是系列电影就是空值）/TMDB上的ID/海报和背景图链接，json格式\n",
    "\n",
    "budget:电影的预算，0代表未知\n",
    "\n",
    "genres:电影的类型以及类型对应的TMDB上的ID，使用json格式封装信息\n",
    "\n",
    "homepage:电影官方主页\n",
    "\n",
    "imdb_id：电影在TMDB的ID\n",
    "\n",
    "orginal_language：电影的原始语言\n",
    "\n",
    "orginal_title：电影的原始名称\n",
    "\n",
    "overview：简短的描述\n",
    "\n",
    "popularity：电影的流行程度，使用浮点数代表\n",
    "\n",
    "poster_path：电影海报链接\n",
    "\n",
    "production_copanies：电影的出品公司，使用json格式\n",
    "\n",
    "production_countries: 电影出品公司所在国家，使用json格式\n",
    "\n",
    "release_date：发行时间\n",
    "\n",
    "runtime：电影时长\n",
    "\n",
    "spoken_languages：电影语言，json格式\n",
    "\n",
    "status：电影的状态，是否已经发布\n",
    "\n",
    "tagline：电影的宣传标语\n",
    "\n",
    "title：电影英文名\n",
    "\n",
    "keywords：电影的关键词以及相应关键词在TMDB上的ID\n",
    "\n",
    "cast：演员的姓名/id/性别，使用json格式\n",
    "\n",
    "crew：职员（导演/编辑/摄影...）的姓名/id/性别，使用json格式\n",
    "\n",
    "revenue：电影总收入\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据清洗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "belongs_to_collection    5917\n",
       "homepage                 5032\n",
       "revenue                  4398\n",
       "tagline                  1460\n",
       "Keywords                  669\n",
       "production_companies      414\n",
       "production_countries      157\n",
       "spoken_languages           62\n",
       "crew                       38\n",
       "cast                       26\n",
       "genres                     23\n",
       "overview                   22\n",
       "runtime                     6\n",
       "title                       3\n",
       "poster_path                 2\n",
       "status                      2\n",
       "release_date                1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def null_count(df):\n",
    "    \"\"\"\n",
    "    空值信息统计\n",
    "    \"\"\"\n",
    "    ans = df.isnull().sum()\n",
    "    ans = ans[ans>0].sort_values(ascending=False)\n",
    "    return ans\n",
    "null_count(data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'id': 313576, 'name': 'Hot Tub Time Machine Collection', 'poster_path': '/iEhb00TGPucF0b4joM1ieyY026U.jpg', 'backdrop_path': '/noeTVcgpBiD48fDjFVic1Vz7ope.jpg'}]\n",
      "\n",
      "[{'id': 107674, 'name': 'The Princess Diaries Collection', 'poster_path': '/wt5AMbxPTS4Kfjx7Fgm149qPfZl.jpg', 'backdrop_path': '/zSEtYD77pKRJlUPx34BJgUG9v1c.jpg'}]\n",
      "\n",
      "[{'id': 256377, 'name': 'The Muppet Collection', 'poster_path': '/8Ew8EIdFFurMMYjSbWPu1Hl4vLX.jpg', 'backdrop_path': '/1AWd3MM90G47mxtD112gRDxSXY9.jpg'}]\n",
      "\n",
      "[{'id': 1575, 'name': 'Rocky Collection', 'poster_path': '/mCY5dMkSSFQufGCViI6jNUU6pXq.jpg', 'backdrop_path': '/w4h6gjdWPvmu5R9H6zeGDPo1ZuV.jpg'}]\n",
      "\n",
      "[{'id': 48190, 'name': 'Revenge of the Nerds Collection', 'poster_path': '/qOnoXEdrSnBuS3FMAFRIgyJSM2r.jpg', 'backdrop_path': None}]\n",
      "\n",
      "[{'id': 91698, 'name': 'Chili Palmer Collection', 'poster_path': '/ae3smJDdWrMJ77tDpYOrpo4frKq.jpg', 'backdrop_path': '/uWaANGQeoSs5vSP1CWtlkDrkqei.jpg'}]\n",
      "\n",
      "[{'id': 9518, 'name': 'The Transporter Collection', 'poster_path': '/uakYnYtFxqjS8pjEWW9lGWmdMRz.jpg', 'backdrop_path': '/qDuTIGAEbxF3jP9zNILLAoxNLpU.jpg'}]\n",
      "\n",
      "[{'id': 9735, 'name': 'Friday the 13th Collection', 'poster_path': '/uobgqpLQff9WvxGKE2OSvXv1RHm.jpg', 'backdrop_path': '/c7pMKwv5NzIN6N3KM4L8fYMTtPw.jpg'}]\n",
      "\n",
      "[{'id': 207621, 'name': 'V/H/S Collection', 'poster_path': '/esfk62fcqTWqB90dAHaVMbDWmbM.jpg', 'backdrop_path': '/wSdVm0uxEvYlwMxOAhipUUzey4w.jpg'}]\n",
      "\n",
      "[{'id': 207632, 'name': 'The ABCs of Death Collection', 'poster_path': '/fzrKAtcg75Je7N0NereOdKePHDM.jpg', 'backdrop_path': None}]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def print_feat(df, col,n=10):\n",
    "    \"\"\"\n",
    "    输出某个特征的前n个非空值\n",
    "    \"\"\"\n",
    "    feature = df[col][df[col].notnull()].head(n)\n",
    "    for item in feature:\n",
    "        print(item)\n",
    "        print()\n",
    "print_feat(data, \"belongs_to_collection\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## belongs_to_collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# belongs_to_collection:\n",
    "# 这个电影系列的系列名称（不是系列电影就是空值）/TMDB上的ID/海报和背景图链接，\n",
    "# json格式,\n",
    "# 增加一列collection,1表示是系列电影，0表示不是系列电影\n",
    "data['collection'] = data['belongs_to_collection'].map(lambda x: 1 if type(x)==str else 0)\n",
    "data['collection'].sum() == data['belongs_to_collection'].notnull().sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## homage, tagline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# homepage:电影官方主页\n",
    "# tagline：电影的宣传标语\n",
    "# 增加两列 has_homepage, has_tagline表示是否有官方主页和宣传标语\n",
    "data['has_homepage'] = data['homepage'].map(lambda x:1 if type(x)==str else 0)\n",
    "data['has_tagline'] = data['tagline'].map(lambda x: 1 if type(x) == str else 0)\n",
    "data['has_homepage'].sum() == data['homepage'].notnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['has_tagline'].sum() == data['tagline'].notnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## overview, title, poster_path, status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# overview：简短的描述\n",
    "#title: 英文名\n",
    "# poster_path ;海报链接\n",
    "# status:状态是否已发行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>belongs_to_collection</th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>homepage</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>overview</th>\n",
       "      <th>popularity</th>\n",
       "      <th>poster_path</th>\n",
       "      <th>...</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title</th>\n",
       "      <th>Keywords</th>\n",
       "      <th>cast</th>\n",
       "      <th>crew</th>\n",
       "      <th>revenue</th>\n",
       "      <th>collection</th>\n",
       "      <th>has_homepage</th>\n",
       "      <th>has_tagline</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6843500</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt2550838</td>\n",
       "      <td>it</td>\n",
       "      <td>Il peggior Natale della mia vita</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.800073</td>\n",
       "      <td>/jflOmKG2sBVzxS36YtDQZQAGUBr.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The Worst Christmas of My Life</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'cast_id': 2, 'character': 'Alberto', 'credi...</td>\n",
       "      <td>[{'credit_id': '52fe4bb79251416c7510bc79', 'de...</td>\n",
       "      <td>10703234.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 35, 'name...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0768690</td>\n",
       "      <td>ru</td>\n",
       "      <td>А поутру они проснулись</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402368</td>\n",
       "      <td>/xiRrq1elN5O0WaDX19nFc9MxeDl.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>А поутру они проснулись</td>\n",
       "      <td>[{'id': 4897, 'name': 'multiple character'}, {...</td>\n",
       "      <td>[{'cast_id': 8, 'character': '', 'credit_id': ...</td>\n",
       "      <td>[{'credit_id': '52fe4d449251416c911103f5', 'de...</td>\n",
       "      <td>234748.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11000000</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt2076251</td>\n",
       "      <td>it</td>\n",
       "      <td>La peggior settimana della mia vita</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.010563</td>\n",
       "      <td>/1DWGUBXRhXrKgLsV6zppldD9bRn.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>La peggior settimana della mia vita</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'cast_id': 4, 'character': 'Paolo', 'credit_...</td>\n",
       "      <td>[{'credit_id': '52fe4984c3a368484e12f923', 'de...</td>\n",
       "      <td>12935800.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6000000</td>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt1107828</td>\n",
       "      <td>ru</td>\n",
       "      <td>Королёв</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.292296</td>\n",
       "      <td>/2XcRiIvliLUUGqv5qOvpc76WeCU.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Королёв</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'cast_id': 3, 'character': '–°–µ—Ä–≥–µ–π –ö–...</td>\n",
       "      <td>[{'credit_id': '52fe4db29251416c7513d819', 'de...</td>\n",
       "      <td>31000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1542</th>\n",
       "      <td>NaN</td>\n",
       "      <td>750000</td>\n",
       "      <td>[{'id': 80, 'name': 'Crime'}, {'id': 35, 'name...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt3805180</td>\n",
       "      <td>ru</td>\n",
       "      <td>Все и сразу</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.201582</td>\n",
       "      <td>/hNsmPpl3zLG36jr4EIEd5P8I4pa.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>All at Once</td>\n",
       "      <td>[{'id': 642, 'name': 'robbery'}, {'id': 231149...</td>\n",
       "      <td>[{'cast_id': 3, 'character': 'Viktor', 'credit...</td>\n",
       "      <td>[{'credit_id': '53b7a7b90e0a2676b8006ab6', 'de...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2151</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000</td>\n",
       "      <td>[{'id': 28, 'name': 'Action'}, {'id': 10749, '...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0477337</td>\n",
       "      <td>ru</td>\n",
       "      <td>Mechenosets</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.414793</td>\n",
       "      <td>/be78nAT5VLN2ETLeqAhBkjVScn5.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mechenosets</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'cast_id': 2, 'character': 'Sasha', 'credit_...</td>\n",
       "      <td>[{'credit_id': '52fe46bec3a368484e0a14f7', 'de...</td>\n",
       "      <td>3919731.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2303</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0116485</td>\n",
       "      <td>de</td>\n",
       "      <td>Happy Weekend</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.002229</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Happy Weekend</td>\n",
       "      <td>[{'id': 572, 'name': 'sex'}, {'id': 596, 'name...</td>\n",
       "      <td>[{'cast_id': 0, 'character': 'Joachim Krippo',...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65335.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2866</th>\n",
       "      <td>[{'id': 188197, 'name': 'Cetto La Qualunque - ...</td>\n",
       "      <td>5579750</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt2456720</td>\n",
       "      <td>it</td>\n",
       "      <td>Tutto tutto niente niente</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.208906</td>\n",
       "      <td>/o5kFD5Xw5kTEiDrdnJJXOXy2jaI.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tutto tutto niente niente</td>\n",
       "      <td>[{'id': 8201, 'name': 'satire'}, {'id': 33501,...</td>\n",
       "      <td>[{'cast_id': 1, 'character': 'Cetto La Qualunq...</td>\n",
       "      <td>[{'credit_id': '52fe4c48c3a36847f82269b9', 'de...</td>\n",
       "      <td>8927600.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3244</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0082131</td>\n",
       "      <td>es</td>\n",
       "      <td>La caliente niña Julietta</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000464</td>\n",
       "      <td>/6ANZPgGGzplpAUuqx97nmAtmgEH.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>La caliente niña Julietta</td>\n",
       "      <td>[{'id': 293, 'name': 'female nudity'}, {'id': ...</td>\n",
       "      <td>[{'cast_id': 7, 'character': 'Julietta Santigo...</td>\n",
       "      <td>[{'credit_id': '55678cf992514156b600069f', 'de...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4418</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 36, 'name...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt1506452</td>\n",
       "      <td>ru</td>\n",
       "      <td>Поп</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.061297</td>\n",
       "      <td>/aaWH5PVxVxnRY35H27q3wo3ax5Z.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The Priest</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'cast_id': 2, 'character': 'Otets Aleksandr'...</td>\n",
       "      <td>[{'credit_id': '52fe47ebc3a36847f814f183', 'de...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4431</th>\n",
       "      <td>NaN</td>\n",
       "      <td>336029</td>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 35, 'name...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt1224449</td>\n",
       "      <td>ru</td>\n",
       "      <td>Плюс один</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.562568</td>\n",
       "      <td>/uhw6P80Al8TrbjK0mZ6yOqlzxu8.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Plus one</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'cast_id': 1, 'character': 'Masha', 'credit_...</td>\n",
       "      <td>[{'credit_id': '52fe45d9c3a36847f80ddfe7', 'de...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4490</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt3132094</td>\n",
       "      <td>es</td>\n",
       "      <td>Pancho, el perro millonario</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.661399</td>\n",
       "      <td>/h1n1RXda54WmuFmrcPU7iXZN0ta.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pancho, el perro millonario</td>\n",
       "      <td>[{'id': 11493, 'name': 'animal as human'}, {'i...</td>\n",
       "      <td>[{'cast_id': 3, 'character': 'Alberto', 'credi...</td>\n",
       "      <td>[{'credit_id': '52fe4e1c9251416c91126d43', 'de...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4633</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0078010</td>\n",
       "      <td>es</td>\n",
       "      <td>Nunca en horas de clase</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.281500</td>\n",
       "      <td>/wVDY6zJV5GCkZF07CMYE8s37Rx7.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nunca en horas de clase</td>\n",
       "      <td>[{'id': 107, 'name': 'barcelona spain'}, {'id'...</td>\n",
       "      <td>[{'cast_id': 6, 'character': 'Susy', 'credit_i...</td>\n",
       "      <td>[{'credit_id': '544d2dd20e0a2608c2002879', 'de...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4832</th>\n",
       "      <td>NaN</td>\n",
       "      <td>15000000</td>\n",
       "      <td>[{'id': 28, 'name': 'Action'}, {'id': 10769, '...</td>\n",
       "      <td>http://drona.erosentertainment.com/</td>\n",
       "      <td>tt1060249</td>\n",
       "      <td>hi</td>\n",
       "      <td>Drona</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.758985</td>\n",
       "      <td>/8DSbr1xMr8Wkoi7vHEe3MJKf7UN.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Drona</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'cast_id': 1, 'character': 'Aditya/Drona', '...</td>\n",
       "      <td>[{'credit_id': '594828d39251413fb1047a81', 'de...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5520</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt1620464</td>\n",
       "      <td>ru</td>\n",
       "      <td>Glukhar v kino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.209434</td>\n",
       "      <td>/vW6feGrSTPWCvRMN966znoUSrgk.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Glukhar v kino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5585</th>\n",
       "      <td>NaN</td>\n",
       "      <td>22000000</td>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 35, 'name...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0114296</td>\n",
       "      <td>sv</td>\n",
       "      <td>Roommates</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.395867</td>\n",
       "      <td>/hvHNlMvWS2GBt7RR971bJ3k4bJc.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Roommates</td>\n",
       "      <td>[{'id': 1158, 'name': 'grandfather grandson re...</td>\n",
       "      <td>[{'cast_id': 1, 'character': 'Rocky Holzeck', ...</td>\n",
       "      <td>[{'credit_id': '5420cabcc3a36818c3000390', 'de...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5845</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt4216934</td>\n",
       "      <td>de</td>\n",
       "      <td>Frau Müller muss weg!</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.144310</td>\n",
       "      <td>/xPWW8EL87kJpENHTlbEKYXpwb30.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Frau Müller muss weg!</td>\n",
       "      <td>[{'id': 10508, 'name': 'teacher'}, {'id': 1087...</td>\n",
       "      <td>[{'cast_id': 0, 'character': 'Frau M√ºller', '...</td>\n",
       "      <td>[{'credit_id': '56b5e384c3a36806f8007f78', 'de...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6210</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3800000</td>\n",
       "      <td>[{'id': 80, 'name': 'Crime'}, {'id': 18, 'name...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0191076</td>\n",
       "      <td>fr</td>\n",
       "      <td>Le dernier souffle</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.072704</td>\n",
       "      <td>/ikGmePZTZLz4Y00j24yBaG8cepZ.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The Last Breath</td>\n",
       "      <td>[{'id': 10714, 'name': 'serial killer'}]</td>\n",
       "      <td>[{'cast_id': 0, 'character': 'Lauren Vaillanco...</td>\n",
       "      <td>[{'credit_id': '53e55e69c3a3684442000231', 'de...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6818</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt2192844</td>\n",
       "      <td>fi</td>\n",
       "      <td>Miesten välisiä keskusteluja</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.011427</td>\n",
       "      <td>/iGykvX2LgYFz9EKAjqGoni9xmCf.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Miesten välisiä keskusteluja</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'cast_id': 0, 'character': 'Kari Mairisaari'...</td>\n",
       "      <td>[{'credit_id': '57cad03a9251411ac50030a1', 'de...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6828</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 10749, '...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0192766</td>\n",
       "      <td>en</td>\n",
       "      <td>A Wake in Providence</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.002817</td>\n",
       "      <td>/nJ3e8zWZskygq8KVgrFhFGRpjw3.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A Wake in Providence</td>\n",
       "      <td>[{'id': 5713, 'name': 'rhode island'}, {'id': ...</td>\n",
       "      <td>[{'cast_id': 10, 'character': 'Anthony', 'cred...</td>\n",
       "      <td>[{'credit_id': '52fe4b2dc3a36847f81f67f9', 'de...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6992</th>\n",
       "      <td>[{'id': 441061, 'name': \"L'allenatore nel pall...</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt1073654</td>\n",
       "      <td>it</td>\n",
       "      <td>L'allenatore nel pallone 2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.773828</td>\n",
       "      <td>/nZbSJgmaMSZXqPfSrldymIFzxT2.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L'allenatore nel pallone 2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'cast_id': 1, 'character': 'Oronzo Can√†', '...</td>\n",
       "      <td>[{'credit_id': '57f16cd6c3a3683f2700f3ea', 'de...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7321</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt1190905</td>\n",
       "      <td>es</td>\n",
       "      <td>El truco del manco</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.191219</td>\n",
       "      <td>/n2C17S0FO4Q0XCYesHxWwgBiuei.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>El truco del manco</td>\n",
       "      <td>[{'id': 898, 'name': 'hip-hop'}, {'id': 6027, ...</td>\n",
       "      <td>[{'cast_id': 6, 'character': \"Quique Heredia '...</td>\n",
       "      <td>[{'credit_id': '5509f03cc3a3682832000044', 'de...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  belongs_to_collection    budget  \\\n",
       "id                                                                  \n",
       "391                                                 NaN   6843500   \n",
       "592                                                 NaN         0   \n",
       "978                                                 NaN  11000000   \n",
       "1336                                                NaN   6000000   \n",
       "1542                                                NaN    750000   \n",
       "2151                                                NaN   5000000   \n",
       "2303                                                NaN         0   \n",
       "2866  [{'id': 188197, 'name': 'Cetto La Qualunque - ...   5579750   \n",
       "3244                                                NaN         0   \n",
       "4418                                                NaN         0   \n",
       "4431                                                NaN    336029   \n",
       "4490                                                NaN         0   \n",
       "4633                                                NaN         0   \n",
       "4832                                                NaN  15000000   \n",
       "5520                                                NaN   2500000   \n",
       "5585                                                NaN  22000000   \n",
       "5845                                                NaN         0   \n",
       "6210                                                NaN   3800000   \n",
       "6818                                                NaN         0   \n",
       "6828                                                NaN         0   \n",
       "6992  [{'id': 441061, 'name': \"L'allenatore nel pall...         0   \n",
       "7321                                                NaN         0   \n",
       "\n",
       "                                                 genres  \\\n",
       "id                                                        \n",
       "391                      [{'id': 35, 'name': 'Comedy'}]   \n",
       "592   [{'id': 18, 'name': 'Drama'}, {'id': 35, 'name...   \n",
       "978                      [{'id': 35, 'name': 'Comedy'}]   \n",
       "1336                      [{'id': 18, 'name': 'Drama'}]   \n",
       "1542  [{'id': 80, 'name': 'Crime'}, {'id': 35, 'name...   \n",
       "2151  [{'id': 28, 'name': 'Action'}, {'id': 10749, '...   \n",
       "2303                     [{'id': 35, 'name': 'Comedy'}]   \n",
       "2866                     [{'id': 35, 'name': 'Comedy'}]   \n",
       "3244                     [{'id': 35, 'name': 'Comedy'}]   \n",
       "4418  [{'id': 18, 'name': 'Drama'}, {'id': 36, 'name...   \n",
       "4431  [{'id': 18, 'name': 'Drama'}, {'id': 35, 'name...   \n",
       "4490                     [{'id': 35, 'name': 'Comedy'}]   \n",
       "4633                     [{'id': 35, 'name': 'Comedy'}]   \n",
       "4832  [{'id': 28, 'name': 'Action'}, {'id': 10769, '...   \n",
       "5520                                                NaN   \n",
       "5585  [{'id': 18, 'name': 'Drama'}, {'id': 35, 'name...   \n",
       "5845                     [{'id': 35, 'name': 'Comedy'}]   \n",
       "6210  [{'id': 80, 'name': 'Crime'}, {'id': 18, 'name...   \n",
       "6818                                                NaN   \n",
       "6828  [{'id': 35, 'name': 'Comedy'}, {'id': 10749, '...   \n",
       "6992                     [{'id': 35, 'name': 'Comedy'}]   \n",
       "7321                      [{'id': 18, 'name': 'Drama'}]   \n",
       "\n",
       "                                 homepage    imdb_id original_language  \\\n",
       "id                                                                       \n",
       "391                                   NaN  tt2550838                it   \n",
       "592                                   NaN  tt0768690                ru   \n",
       "978                                   NaN  tt2076251                it   \n",
       "1336                                  NaN  tt1107828                ru   \n",
       "1542                                  NaN  tt3805180                ru   \n",
       "2151                                  NaN  tt0477337                ru   \n",
       "2303                                  NaN  tt0116485                de   \n",
       "2866                                  NaN  tt2456720                it   \n",
       "3244                                  NaN  tt0082131                es   \n",
       "4418                                  NaN  tt1506452                ru   \n",
       "4431                                  NaN  tt1224449                ru   \n",
       "4490                                  NaN  tt3132094                es   \n",
       "4633                                  NaN  tt0078010                es   \n",
       "4832  http://drona.erosentertainment.com/  tt1060249                hi   \n",
       "5520                                  NaN  tt1620464                ru   \n",
       "5585                                  NaN  tt0114296                sv   \n",
       "5845                                  NaN  tt4216934                de   \n",
       "6210                                  NaN  tt0191076                fr   \n",
       "6818                                  NaN  tt2192844                fi   \n",
       "6828                                  NaN  tt0192766                en   \n",
       "6992                                  NaN  tt1073654                it   \n",
       "7321                                  NaN  tt1190905                es   \n",
       "\n",
       "                           original_title overview  popularity  \\\n",
       "id                                                               \n",
       "391      Il peggior Natale della mia vita      NaN    3.800073   \n",
       "592               А поутру они проснулись      NaN    0.402368   \n",
       "978   La peggior settimana della mia vita      NaN    5.010563   \n",
       "1336                              Королёв      NaN    0.292296   \n",
       "1542                          Все и сразу      NaN    0.201582   \n",
       "2151                          Mechenosets      NaN    0.414793   \n",
       "2303                        Happy Weekend      NaN    0.002229   \n",
       "2866            Tutto tutto niente niente      NaN    2.208906   \n",
       "3244            La caliente niña Julietta      NaN    0.000464   \n",
       "4418                                  Поп      NaN    1.061297   \n",
       "4431                            Плюс один      NaN    0.562568   \n",
       "4490          Pancho, el perro millonario      NaN    0.661399   \n",
       "4633              Nunca en horas de clase      NaN    0.281500   \n",
       "4832                                Drona      NaN    0.758985   \n",
       "5520                       Glukhar v kino      NaN    0.209434   \n",
       "5585                            Roommates      NaN    3.395867   \n",
       "5845                Frau Müller muss weg!      NaN    2.144310   \n",
       "6210                   Le dernier souffle      NaN    0.072704   \n",
       "6818         Miesten välisiä keskusteluja      NaN    0.011427   \n",
       "6828                 A Wake in Providence      NaN    0.002817   \n",
       "6992           L'allenatore nel pallone 2      NaN    1.773828   \n",
       "7321                   El truco del manco      NaN    0.191219   \n",
       "\n",
       "                           poster_path  ...    status tagline  \\\n",
       "id                                      ...                     \n",
       "391   /jflOmKG2sBVzxS36YtDQZQAGUBr.jpg  ...  Released     NaN   \n",
       "592   /xiRrq1elN5O0WaDX19nFc9MxeDl.jpg  ...  Released     NaN   \n",
       "978   /1DWGUBXRhXrKgLsV6zppldD9bRn.jpg  ...  Released     NaN   \n",
       "1336  /2XcRiIvliLUUGqv5qOvpc76WeCU.jpg  ...  Released     NaN   \n",
       "1542  /hNsmPpl3zLG36jr4EIEd5P8I4pa.jpg  ...  Released     NaN   \n",
       "2151  /be78nAT5VLN2ETLeqAhBkjVScn5.jpg  ...  Released     NaN   \n",
       "2303                               NaN  ...  Released     NaN   \n",
       "2866  /o5kFD5Xw5kTEiDrdnJJXOXy2jaI.jpg  ...  Released     NaN   \n",
       "3244  /6ANZPgGGzplpAUuqx97nmAtmgEH.jpg  ...  Released     NaN   \n",
       "4418  /aaWH5PVxVxnRY35H27q3wo3ax5Z.jpg  ...  Released     NaN   \n",
       "4431  /uhw6P80Al8TrbjK0mZ6yOqlzxu8.jpg  ...  Released     NaN   \n",
       "4490  /h1n1RXda54WmuFmrcPU7iXZN0ta.jpg  ...  Released     NaN   \n",
       "4633  /wVDY6zJV5GCkZF07CMYE8s37Rx7.jpg  ...  Released     NaN   \n",
       "4832  /8DSbr1xMr8Wkoi7vHEe3MJKf7UN.jpg  ...  Released     NaN   \n",
       "5520  /vW6feGrSTPWCvRMN966znoUSrgk.jpg  ...  Released     NaN   \n",
       "5585  /hvHNlMvWS2GBt7RR971bJ3k4bJc.jpg  ...  Released     NaN   \n",
       "5845  /xPWW8EL87kJpENHTlbEKYXpwb30.jpg  ...  Released     NaN   \n",
       "6210  /ikGmePZTZLz4Y00j24yBaG8cepZ.jpg  ...  Released     NaN   \n",
       "6818  /iGykvX2LgYFz9EKAjqGoni9xmCf.jpg  ...  Released     NaN   \n",
       "6828  /nJ3e8zWZskygq8KVgrFhFGRpjw3.jpg  ...  Released     NaN   \n",
       "6992  /nZbSJgmaMSZXqPfSrldymIFzxT2.jpg  ...  Released     NaN   \n",
       "7321  /n2C17S0FO4Q0XCYesHxWwgBiuei.jpg  ...  Released     NaN   \n",
       "\n",
       "                                    title  \\\n",
       "id                                          \n",
       "391        The Worst Christmas of My Life   \n",
       "592               А поутру они проснулись   \n",
       "978   La peggior settimana della mia vita   \n",
       "1336                              Королёв   \n",
       "1542                          All at Once   \n",
       "2151                          Mechenosets   \n",
       "2303                        Happy Weekend   \n",
       "2866            Tutto tutto niente niente   \n",
       "3244            La caliente niña Julietta   \n",
       "4418                           The Priest   \n",
       "4431                             Plus one   \n",
       "4490          Pancho, el perro millonario   \n",
       "4633              Nunca en horas de clase   \n",
       "4832                                Drona   \n",
       "5520                       Glukhar v kino   \n",
       "5585                            Roommates   \n",
       "5845                Frau Müller muss weg!   \n",
       "6210                      The Last Breath   \n",
       "6818         Miesten välisiä keskusteluja   \n",
       "6828                 A Wake in Providence   \n",
       "6992           L'allenatore nel pallone 2   \n",
       "7321                   El truco del manco   \n",
       "\n",
       "                                               Keywords  \\\n",
       "id                                                        \n",
       "391                                                 NaN   \n",
       "592   [{'id': 4897, 'name': 'multiple character'}, {...   \n",
       "978                                                 NaN   \n",
       "1336                                                NaN   \n",
       "1542  [{'id': 642, 'name': 'robbery'}, {'id': 231149...   \n",
       "2151                                                NaN   \n",
       "2303  [{'id': 572, 'name': 'sex'}, {'id': 596, 'name...   \n",
       "2866  [{'id': 8201, 'name': 'satire'}, {'id': 33501,...   \n",
       "3244  [{'id': 293, 'name': 'female nudity'}, {'id': ...   \n",
       "4418                                                NaN   \n",
       "4431                                                NaN   \n",
       "4490  [{'id': 11493, 'name': 'animal as human'}, {'i...   \n",
       "4633  [{'id': 107, 'name': 'barcelona spain'}, {'id'...   \n",
       "4832                                                NaN   \n",
       "5520                                                NaN   \n",
       "5585  [{'id': 1158, 'name': 'grandfather grandson re...   \n",
       "5845  [{'id': 10508, 'name': 'teacher'}, {'id': 1087...   \n",
       "6210           [{'id': 10714, 'name': 'serial killer'}]   \n",
       "6818                                                NaN   \n",
       "6828  [{'id': 5713, 'name': 'rhode island'}, {'id': ...   \n",
       "6992                                                NaN   \n",
       "7321  [{'id': 898, 'name': 'hip-hop'}, {'id': 6027, ...   \n",
       "\n",
       "                                                   cast  \\\n",
       "id                                                        \n",
       "391   [{'cast_id': 2, 'character': 'Alberto', 'credi...   \n",
       "592   [{'cast_id': 8, 'character': '', 'credit_id': ...   \n",
       "978   [{'cast_id': 4, 'character': 'Paolo', 'credit_...   \n",
       "1336  [{'cast_id': 3, 'character': '–°–µ—Ä–≥–µ–π –ö–...   \n",
       "1542  [{'cast_id': 3, 'character': 'Viktor', 'credit...   \n",
       "2151  [{'cast_id': 2, 'character': 'Sasha', 'credit_...   \n",
       "2303  [{'cast_id': 0, 'character': 'Joachim Krippo',...   \n",
       "2866  [{'cast_id': 1, 'character': 'Cetto La Qualunq...   \n",
       "3244  [{'cast_id': 7, 'character': 'Julietta Santigo...   \n",
       "4418  [{'cast_id': 2, 'character': 'Otets Aleksandr'...   \n",
       "4431  [{'cast_id': 1, 'character': 'Masha', 'credit_...   \n",
       "4490  [{'cast_id': 3, 'character': 'Alberto', 'credi...   \n",
       "4633  [{'cast_id': 6, 'character': 'Susy', 'credit_i...   \n",
       "4832  [{'cast_id': 1, 'character': 'Aditya/Drona', '...   \n",
       "5520                                                 []   \n",
       "5585  [{'cast_id': 1, 'character': 'Rocky Holzeck', ...   \n",
       "5845  [{'cast_id': 0, 'character': 'Frau M√ºller', '...   \n",
       "6210  [{'cast_id': 0, 'character': 'Lauren Vaillanco...   \n",
       "6818  [{'cast_id': 0, 'character': 'Kari Mairisaari'...   \n",
       "6828  [{'cast_id': 10, 'character': 'Anthony', 'cred...   \n",
       "6992  [{'cast_id': 1, 'character': 'Oronzo Can√†', '...   \n",
       "7321  [{'cast_id': 6, 'character': \"Quique Heredia '...   \n",
       "\n",
       "                                                   crew     revenue  \\\n",
       "id                                                                    \n",
       "391   [{'credit_id': '52fe4bb79251416c7510bc79', 'de...  10703234.0   \n",
       "592   [{'credit_id': '52fe4d449251416c911103f5', 'de...    234748.0   \n",
       "978   [{'credit_id': '52fe4984c3a368484e12f923', 'de...  12935800.0   \n",
       "1336  [{'credit_id': '52fe4db29251416c7513d819', 'de...     31000.0   \n",
       "1542  [{'credit_id': '53b7a7b90e0a2676b8006ab6', 'de...         3.0   \n",
       "2151  [{'credit_id': '52fe46bec3a368484e0a14f7', 'de...   3919731.0   \n",
       "2303                                                NaN     65335.0   \n",
       "2866  [{'credit_id': '52fe4c48c3a36847f82269b9', 'de...   8927600.0   \n",
       "3244  [{'credit_id': '55678cf992514156b600069f', 'de...         NaN   \n",
       "4418  [{'credit_id': '52fe47ebc3a36847f814f183', 'de...         NaN   \n",
       "4431  [{'credit_id': '52fe45d9c3a36847f80ddfe7', 'de...         NaN   \n",
       "4490  [{'credit_id': '52fe4e1c9251416c91126d43', 'de...         NaN   \n",
       "4633  [{'credit_id': '544d2dd20e0a2608c2002879', 'de...         NaN   \n",
       "4832  [{'credit_id': '594828d39251413fb1047a81', 'de...         NaN   \n",
       "5520                                                NaN         NaN   \n",
       "5585  [{'credit_id': '5420cabcc3a36818c3000390', 'de...         NaN   \n",
       "5845  [{'credit_id': '56b5e384c3a36806f8007f78', 'de...         NaN   \n",
       "6210  [{'credit_id': '53e55e69c3a3684442000231', 'de...         NaN   \n",
       "6818  [{'credit_id': '57cad03a9251411ac50030a1', 'de...         NaN   \n",
       "6828  [{'credit_id': '52fe4b2dc3a36847f81f67f9', 'de...         NaN   \n",
       "6992  [{'credit_id': '57f16cd6c3a3683f2700f3ea', 'de...         NaN   \n",
       "7321  [{'credit_id': '5509f03cc3a3682832000044', 'de...         NaN   \n",
       "\n",
       "     collection has_homepage has_tagline  \n",
       "id                                        \n",
       "391           0            0           0  \n",
       "592           0            0           0  \n",
       "978           0            0           0  \n",
       "1336          0            0           0  \n",
       "1542          0            0           0  \n",
       "2151          0            0           0  \n",
       "2303          0            0           0  \n",
       "2866          1            0           0  \n",
       "3244          0            0           0  \n",
       "4418          0            0           0  \n",
       "4431          0            0           0  \n",
       "4490          0            0           0  \n",
       "4633          0            0           0  \n",
       "4832          0            1           0  \n",
       "5520          0            0           0  \n",
       "5585          0            0           0  \n",
       "5845          0            0           0  \n",
       "6210          0            0           0  \n",
       "6818          0            0           0  \n",
       "6828          0            0           0  \n",
       "6992          1            0           0  \n",
       "7321          0            0           0  \n",
       "\n",
       "[22 rows x 25 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['overview'].isnull()] # 增加一列表示是否有简短描述"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['has_overview'] = data['overview'].map(lambda x: 1 if type(x)==str else 0)\n",
    "data['has_overview'].sum() == data['overview'].notnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# title                       3\n",
    "# poster_path                 2\n",
    "# status                      2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>belongs_to_collection</th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>homepage</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>overview</th>\n",
       "      <th>popularity</th>\n",
       "      <th>poster_path</th>\n",
       "      <th>...</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title</th>\n",
       "      <th>Keywords</th>\n",
       "      <th>cast</th>\n",
       "      <th>crew</th>\n",
       "      <th>revenue</th>\n",
       "      <th>collection</th>\n",
       "      <th>has_homepage</th>\n",
       "      <th>has_tagline</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5399</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 16, 'name...</td>\n",
       "      <td>http://wwws.warnerbros.co.jp/budori/</td>\n",
       "      <td>tt2391821</td>\n",
       "      <td>ja</td>\n",
       "      <td>グスコーブドリの伝記</td>\n",
       "      <td>Remake of The Life of Guskou Budori (1994).\\n ...</td>\n",
       "      <td>0.394173</td>\n",
       "      <td>/hm2z04C3AeerpHeRHqhi9dwu5Gi.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'cast_id': 3, 'character': 'Boduri', 'credit...</td>\n",
       "      <td>[{'credit_id': '52fe4bd7c3a36847f8216183', 'de...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5426</th>\n",
       "      <td>[{'id': 115840, 'name': 'Would I Lie to You? C...</td>\n",
       "      <td>25496629</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
       "      <td>http://www.laveritesijemens3.com/</td>\n",
       "      <td>tt1794850</td>\n",
       "      <td>fr</td>\n",
       "      <td>La Vérité si je Mens ! 3</td>\n",
       "      <td>Eddie, Dov, Yvan et les autres‚Ä¶ Nos chaleure...</td>\n",
       "      <td>3.815840</td>\n",
       "      <td>/oavfuwAr7LNWRTX3qwdPWxV4Ywa.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'cast_id': 2, 'character': 'Eddie', 'credit_...</td>\n",
       "      <td>[{'credit_id': '552bb9a09251417c29001c7a', 'de...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6629</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6000000</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...</td>\n",
       "      <td>http://barefootthemovie.com/</td>\n",
       "      <td>tt2355495</td>\n",
       "      <td>en</td>\n",
       "      <td>Barefoot</td>\n",
       "      <td>The \"black sheep\" son of a wealthy family meet...</td>\n",
       "      <td>5.939334</td>\n",
       "      <td>/m8iFS5IW1LSEX0and2FEWzy6GCX.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'id': 2487, 'name': 'naivety'}, {'id': 9714,...</td>\n",
       "      <td>[{'cast_id': 3, 'character': 'Daisy', 'credit_...</td>\n",
       "      <td>[{'credit_id': '52fe4ec2c3a36847f82a65a7', 'de...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  belongs_to_collection    budget  \\\n",
       "id                                                                  \n",
       "5399                                                NaN         0   \n",
       "5426  [{'id': 115840, 'name': 'Would I Lie to You? C...  25496629   \n",
       "6629                                                NaN   6000000   \n",
       "\n",
       "                                                 genres  \\\n",
       "id                                                        \n",
       "5399  [{'id': 18, 'name': 'Drama'}, {'id': 16, 'name...   \n",
       "5426                     [{'id': 35, 'name': 'Comedy'}]   \n",
       "6629  [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...   \n",
       "\n",
       "                                  homepage    imdb_id original_language  \\\n",
       "id                                                                        \n",
       "5399  http://wwws.warnerbros.co.jp/budori/  tt2391821                ja   \n",
       "5426     http://www.laveritesijemens3.com/  tt1794850                fr   \n",
       "6629          http://barefootthemovie.com/  tt2355495                en   \n",
       "\n",
       "                original_title  \\\n",
       "id                               \n",
       "5399                グスコーブドリの伝記   \n",
       "5426  La Vérité si je Mens ! 3   \n",
       "6629                  Barefoot   \n",
       "\n",
       "                                               overview  popularity  \\\n",
       "id                                                                    \n",
       "5399  Remake of The Life of Guskou Budori (1994).\\n ...    0.394173   \n",
       "5426  Eddie, Dov, Yvan et les autres‚Ä¶ Nos chaleure...    3.815840   \n",
       "6629  The \"black sheep\" son of a wealthy family meet...    5.939334   \n",
       "\n",
       "                           poster_path  ...    status tagline title  \\\n",
       "id                                      ...                           \n",
       "5399  /hm2z04C3AeerpHeRHqhi9dwu5Gi.jpg  ...  Released     NaN   NaN   \n",
       "5426  /oavfuwAr7LNWRTX3qwdPWxV4Ywa.jpg  ...  Released     NaN   NaN   \n",
       "6629  /m8iFS5IW1LSEX0and2FEWzy6GCX.jpg  ...  Released     NaN   NaN   \n",
       "\n",
       "                                               Keywords  \\\n",
       "id                                                        \n",
       "5399                                                NaN   \n",
       "5426                                                NaN   \n",
       "6629  [{'id': 2487, 'name': 'naivety'}, {'id': 9714,...   \n",
       "\n",
       "                                                   cast  \\\n",
       "id                                                        \n",
       "5399  [{'cast_id': 3, 'character': 'Boduri', 'credit...   \n",
       "5426  [{'cast_id': 2, 'character': 'Eddie', 'credit_...   \n",
       "6629  [{'cast_id': 3, 'character': 'Daisy', 'credit_...   \n",
       "\n",
       "                                                   crew revenue collection  \\\n",
       "id                                                                           \n",
       "5399  [{'credit_id': '52fe4bd7c3a36847f8216183', 'de...     NaN          0   \n",
       "5426  [{'credit_id': '552bb9a09251417c29001c7a', 'de...     NaN          1   \n",
       "6629  [{'credit_id': '52fe4ec2c3a36847f82a65a7', 'de...     NaN          0   \n",
       "\n",
       "     has_homepage has_tagline  \n",
       "id                             \n",
       "5399            1           0  \n",
       "5426            1           0  \n",
       "6629            1           0  \n",
       "\n",
       "[3 rows x 25 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.title.isnull()] #  全部来自与预测值，删去该特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(\"title\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>belongs_to_collection</th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>homepage</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>overview</th>\n",
       "      <th>popularity</th>\n",
       "      <th>poster_path</th>\n",
       "      <th>...</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>Keywords</th>\n",
       "      <th>cast</th>\n",
       "      <th>crew</th>\n",
       "      <th>revenue</th>\n",
       "      <th>collection</th>\n",
       "      <th>has_homepage</th>\n",
       "      <th>has_tagline</th>\n",
       "      <th>has_overview</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2303</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0116485</td>\n",
       "      <td>de</td>\n",
       "      <td>Happy Weekend</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.002229</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'id': 572, 'name': 'sex'}, {'id': 596, 'name...</td>\n",
       "      <td>[{'cast_id': 0, 'character': 'Joachim Krippo',...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65335.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3829</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0210130</td>\n",
       "      <td>en</td>\n",
       "      <td>Jails, Hospitals &amp; Hip-Hop</td>\n",
       "      <td>Jails, Hospitals &amp;amp; Hip-Hop is a cinematic ...</td>\n",
       "      <td>0.009057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>three worlds / two million voices / one genera...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     belongs_to_collection  budget                          genres homepage  \\\n",
       "id                                                                            \n",
       "2303                   NaN       0  [{'id': 35, 'name': 'Comedy'}]      NaN   \n",
       "3829                   NaN       0   [{'id': 18, 'name': 'Drama'}]      NaN   \n",
       "\n",
       "        imdb_id original_language              original_title  \\\n",
       "id                                                              \n",
       "2303  tt0116485                de               Happy Weekend   \n",
       "3829  tt0210130                en  Jails, Hospitals & Hip-Hop   \n",
       "\n",
       "                                               overview  popularity  \\\n",
       "id                                                                    \n",
       "2303                                                NaN    0.002229   \n",
       "3829  Jails, Hospitals &amp; Hip-Hop is a cinematic ...    0.009057   \n",
       "\n",
       "     poster_path  ...    status  \\\n",
       "id                ...             \n",
       "2303         NaN  ...  Released   \n",
       "3829         NaN  ...       NaN   \n",
       "\n",
       "                                                tagline  \\\n",
       "id                                                        \n",
       "2303                                                NaN   \n",
       "3829  three worlds / two million voices / one genera...   \n",
       "\n",
       "                                               Keywords  \\\n",
       "id                                                        \n",
       "2303  [{'id': 572, 'name': 'sex'}, {'id': 596, 'name...   \n",
       "3829                                                NaN   \n",
       "\n",
       "                                                   cast crew  revenue  \\\n",
       "id                                                                      \n",
       "2303  [{'cast_id': 0, 'character': 'Joachim Krippo',...  NaN  65335.0   \n",
       "3829                                                 []  NaN      NaN   \n",
       "\n",
       "     collection has_homepage has_tagline has_overview  \n",
       "id                                                     \n",
       "2303          0            0           0            0  \n",
       "3829          0            0           1            1  \n",
       "\n",
       "[2 rows x 25 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.poster_path.isnull()]# 两个空值一个来自训练集，一个来自测试集，删去该特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(\"poster_path\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>belongs_to_collection</th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>homepage</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>overview</th>\n",
       "      <th>popularity</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>...</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>Keywords</th>\n",
       "      <th>cast</th>\n",
       "      <th>crew</th>\n",
       "      <th>revenue</th>\n",
       "      <th>collection</th>\n",
       "      <th>has_homepage</th>\n",
       "      <th>has_tagline</th>\n",
       "      <th>has_overview</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3829</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0210130</td>\n",
       "      <td>en</td>\n",
       "      <td>Jails, Hospitals &amp; Hip-Hop</td>\n",
       "      <td>Jails, Hospitals &amp;amp; Hip-Hop is a cinematic ...</td>\n",
       "      <td>0.009057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>three worlds / two million voices / one genera...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4057</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 99, 'name': 'Documentary'}, {'id': 18,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0334416</td>\n",
       "      <td>en</td>\n",
       "      <td>Stevie</td>\n",
       "      <td>In 1995 Director Steve James (Hoop Dreams) ret...</td>\n",
       "      <td>0.489997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>[{'credit_id': '52fe480dc3a36847f8155e61', 'de...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     belongs_to_collection  budget  \\\n",
       "id                                   \n",
       "3829                   NaN       0   \n",
       "4057                   NaN       0   \n",
       "\n",
       "                                                 genres homepage    imdb_id  \\\n",
       "id                                                                            \n",
       "3829                      [{'id': 18, 'name': 'Drama'}]      NaN  tt0210130   \n",
       "4057  [{'id': 99, 'name': 'Documentary'}, {'id': 18,...      NaN  tt0334416   \n",
       "\n",
       "     original_language              original_title  \\\n",
       "id                                                   \n",
       "3829                en  Jails, Hospitals & Hip-Hop   \n",
       "4057                en                      Stevie   \n",
       "\n",
       "                                               overview  popularity  \\\n",
       "id                                                                    \n",
       "3829  Jails, Hospitals &amp; Hip-Hop is a cinematic ...    0.009057   \n",
       "4057  In 1995 Director Steve James (Hoop Dreams) ret...    0.489997   \n",
       "\n",
       "     production_companies  ... status  \\\n",
       "id                         ...          \n",
       "3829                  NaN  ...    NaN   \n",
       "4057                  NaN  ...    NaN   \n",
       "\n",
       "                                                tagline  Keywords cast  \\\n",
       "id                                                                       \n",
       "3829  three worlds / two million voices / one genera...       NaN   []   \n",
       "4057                                                NaN       NaN   []   \n",
       "\n",
       "                                                   crew revenue collection  \\\n",
       "id                                                                           \n",
       "3829                                                NaN     NaN          0   \n",
       "4057  [{'credit_id': '52fe480dc3a36847f8155e61', 'de...     NaN          0   \n",
       "\n",
       "     has_homepage has_tagline  has_overview  \n",
       "id                                           \n",
       "3829            0           1             1  \n",
       "4057            0           0             1  \n",
       "\n",
       "[2 rows x 24 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.status.isnull()] # 来自测试集，删去该特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(\"status\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把已经处理过的列删除\n",
    "\n",
    "cols = [\"belongs_to_collection\", \"homepage\", \"tagline\",\"overview\"]\n",
    "data.drop(cols,axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "revenue                 4398\n",
       "Keywords                 669\n",
       "production_companies     414\n",
       "production_countries     157\n",
       "spoken_languages          62\n",
       "crew                      38\n",
       "cast                      26\n",
       "genres                    23\n",
       "runtime                    6\n",
       "release_date               1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_count(data) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## runtime, release_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['runtime'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 用平均值填充runtime\n",
    "data['runtime'].fillna(data['runtime'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>popularity</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>production_countries</th>\n",
       "      <th>release_date</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>Keywords</th>\n",
       "      <th>cast</th>\n",
       "      <th>crew</th>\n",
       "      <th>revenue</th>\n",
       "      <th>collection</th>\n",
       "      <th>has_homepage</th>\n",
       "      <th>has_tagline</th>\n",
       "      <th>has_overview</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3829</th>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}]</td>\n",
       "      <td>tt0210130</td>\n",
       "      <td>en</td>\n",
       "      <td>Jails, Hospitals &amp; Hip-Hop</td>\n",
       "      <td>0.009057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      budget                         genres    imdb_id original_language  \\\n",
       "id                                                                         \n",
       "3829       0  [{'id': 18, 'name': 'Drama'}]  tt0210130                en   \n",
       "\n",
       "                  original_title  popularity production_companies  \\\n",
       "id                                                                  \n",
       "3829  Jails, Hospitals & Hip-Hop    0.009057                  NaN   \n",
       "\n",
       "     production_countries release_date  runtime spoken_languages Keywords  \\\n",
       "id                                                                          \n",
       "3829                  NaN          NaN     90.0              NaN      NaN   \n",
       "\n",
       "     cast crew  revenue  collection  has_homepage  has_tagline  has_overview  \n",
       "id                                                                            \n",
       "3829   []  NaN      NaN           0             0            1             1  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.release_date.isnull()]\n",
    "## release为空值的电影是2001-03-20上映"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9/10/10     10\n",
       "9/9/11       8\n",
       "1/16/09      7\n",
       "12/25/14     7\n",
       "9/16/05      7\n",
       "            ..\n",
       "3/25/57      1\n",
       "8/22/12      1\n",
       "4/6/48       1\n",
       "3/6/06       1\n",
       "10/4/07      1\n",
       "Name: release_date, Length: 4703, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.release_date.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dateutil.parser import parse\n",
    "# 增加三列year,month,day 表示上映的年月日\n",
    "dates = data.release_date.map(lambda x: parse(x) if type(x)==str else parse(\"2001-03-20\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id\n",
       "1      2015-02-20\n",
       "2      2004-08-06\n",
       "3      2014-10-10\n",
       "4      2012-03-09\n",
       "5      2009-02-05\n",
       "          ...    \n",
       "7394   2001-08-03\n",
       "7395   2004-08-20\n",
       "7396   1982-12-08\n",
       "7397   2015-02-04\n",
       "7398   2062-09-20\n",
       "Name: release_date, Length: 7398, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates[1].year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['year'] = dates.map(lambda x:x.year)\n",
    "data['month'] = dates.map(lambda x: x.month)\n",
    "data['day'] = dates.map(lambda x: x.day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'9/20/62'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[-1]['release_date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# '9/20/62'实际上是1962年，所以要减去一百\n",
    "data['year'] = data['year'].map(lambda x: x if x<2019 else x-100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7398.000000\n",
       "mean     1999.690727\n",
       "std        15.341220\n",
       "min      1921.000000\n",
       "25%      1992.000000\n",
       "50%      2004.000000\n",
       "75%      2011.000000\n",
       "max      2018.000000\n",
       "Name: year, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['year'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把release_date删去\n",
    "data.drop(\"release_date\", axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keywords                 669\n",
    "# production_companies     414\n",
    "# production_countries     157\n",
    "# spoken_languages          62\n",
    "# crew                      38\n",
    "# cast                      26\n",
    "# genres                    23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data.to_csv('tmdb.csv')\n",
    "# data = pd.read_csv(\"tmdb.csv\",index_col=\"id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['budget', 'genres', 'imdb_id', 'original_language', 'original_title',\n",
       "       'popularity', 'production_companies', 'production_countries', 'runtime',\n",
       "       'spoken_languages', 'Keywords', 'cast', 'crew', 'revenue', 'collection',\n",
       "       'has_homepage', 'has_tagline', 'has_overview', 'year', 'month', 'day'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## spoken_languages, original_language"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'iso_639_1': 'en', 'name': 'English'}]                                                                                                                                                                   4521\n",
       "[{'iso_639_1': 'en', 'name': 'English'}, {'iso_639_1': 'es', 'name': 'Español'}]                                                                                                                            197\n",
       "[{'iso_639_1': 'en', 'name': 'English'}, {'iso_639_1': 'fr', 'name': 'Français'}]                                                                                                                           178\n",
       "[{'iso_639_1': 'ru', 'name': 'Pусский'}]                                                                                                                                                                    103\n",
       "[{'iso_639_1': 'fr', 'name': 'Français'}]                                                                                                                                                                    98\n",
       "                                                                                                                                                                                                           ... \n",
       "[{'iso_639_1': 'ar', 'name': 'العربية'}, {'iso_639_1': 'en', 'name': 'English'}, {'iso_639_1': 'fr', 'name': 'Français'}, {'iso_639_1': 'ro', 'name': 'Română'}]                                              1\n",
       "[{'iso_639_1': 'da', 'name': 'Dansk'}, {'iso_639_1': 'en', 'name': 'English'}, {'iso_639_1': 'fr', 'name': 'Français'}, {'iso_639_1': 'de', 'name': 'Deutsch'}, {'iso_639_1': 'es', 'name': 'Español'}]       1\n",
       "[{'iso_639_1': 'la', 'name': 'Latin'}, {'iso_639_1': 'en', 'name': 'English'}, {'iso_639_1': 'es', 'name': 'Español'}]                                                                                        1\n",
       "[{'iso_639_1': 'cn', 'name': '广州话 / 廣州話'}, {'iso_639_1': 'fr', 'name': 'Français'}, {'iso_639_1': 'es', 'name': 'Español'}]                                                                                   1\n",
       "[{'iso_639_1': 'fr', 'name': 'Français'}, {'iso_639_1': 'ln', 'name': ''}]                                                                                                                                    1\n",
       "Name: spoken_languages, Length: 761, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# spoken_languages可以用original_language来填充\n",
    "data['spoken_languages'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "spoken = data['spoken_languages'].copy(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id\n",
       "1                                             [iso_639_1]\n",
       "2                                             [iso_639_1]\n",
       "3                                             [iso_639_1]\n",
       "4                                  [iso_639_1, iso_639_1]\n",
       "5                                             [iso_639_1]\n",
       "                              ...                        \n",
       "7394    [iso_639_1, iso_639_1, iso_639_1, iso_639_1, i...\n",
       "7395                                          [iso_639_1]\n",
       "7396                                          [iso_639_1]\n",
       "7397                                          [iso_639_1]\n",
       "7398                                          [iso_639_1]\n",
       "Name: spoken_languages, Length: 7398, dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def spoken_process(x):\n",
    "    listx = eval(x) if isinstance(x, str) else  []\n",
    "    ans = []\n",
    "    if isinstance(listx, list):\n",
    "        for i in listx:\n",
    "            ans.append(list(i.keys())[0])\n",
    "    return ans \n",
    "spoken.map(spoken_process)       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'iso_639_1'}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sset = set()\n",
    "for x in spoken.map(spoken_process):\n",
    "    for y in x:\n",
    "        sset.add(y)\n",
    "sset  # spoken_languages中的数据字典序列第一个key都是iso_639_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 44 artists>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = data['original_language'].value_counts()\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.bar(t.index, t.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(44,)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.shape # 总共44种语言"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "en    6351\n",
       "fr     199\n",
       "hi     118\n",
       "ru     109\n",
       "es      95\n",
       "ja      90\n",
       "it      56\n",
       "ko      49\n",
       "de      49\n",
       "zh      46\n",
       "cn      41\n",
       "ta      31\n",
       "sv      20\n",
       "da      17\n",
       "pt      13\n",
       "ml      12\n",
       "nl      11\n",
       "ro       9\n",
       "te       9\n",
       "tr       9\n",
       "he       6\n",
       "th       5\n",
       "pl       5\n",
       "fa       5\n",
       "no       5\n",
       "hu       4\n",
       "fi       4\n",
       "id       3\n",
       "el       3\n",
       "sr       3\n",
       "cs       3\n",
       "bn       3\n",
       "ur       2\n",
       "bm       2\n",
       "xx       2\n",
       "kn       1\n",
       "vi       1\n",
       "ca       1\n",
       "ka       1\n",
       "mr       1\n",
       "is       1\n",
       "af       1\n",
       "nb       1\n",
       "ar       1\n",
       "Name: original_language, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.sort_values(ascending=False)# 选择达到两位数的语言构造新的列，"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "en    6351\n",
       "fr     199\n",
       "hi     118\n",
       "ru     109\n",
       "es      95\n",
       "ja      90\n",
       "it      56\n",
       "ko      49\n",
       "de      49\n",
       "zh      46\n",
       "cn      41\n",
       "ta      31\n",
       "sv      20\n",
       "da      17\n",
       "pt      13\n",
       "ml      12\n",
       "nl      11\n",
       "Name: original_language, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_cosl = t.sort_values(ascending=False)[:17]\n",
    "new_cosl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "original_language = data['original_language'].copy()\n",
    "index = data[data['spoken_languages'].isnull()].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 151,  178,  455,  471,  980, 1102, 1334, 1336, 1484, 1504, 1538,\n",
       "            1623, 1649, 1761, 1918, 1924, 2428, 2630, 2687, 2786, 3084, 3337,\n",
       "            3813, 3829, 3831, 3881, 4057, 4105, 4476, 4508, 4534, 4839, 4841,\n",
       "            4851, 4941, 4969, 5128, 5214, 5252, 5399, 5426, 5469, 5545, 5835,\n",
       "            5899, 6038, 6068, 6085, 6112, 6154, 6209, 6533, 6547, 6583, 6629,\n",
       "            6740, 6748, 6766, 6997, 7014, 7280, 7354],\n",
       "           dtype='int64', name='id')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index  # spoken_languages 为空的index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def spoken_abstract(x):\n",
    "    temp = eval(x) if isinstance(x, str) else []\n",
    "    ans = []\n",
    "    for i in temp:\n",
    "        ans.append(i['iso_639_1'])\n",
    "    return ans\n",
    "    \n",
    "data['spoken_languages'] = data['spoken_languages'].map(spoken_abstract)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id\n",
       "1                       [en]\n",
       "2                       [en]\n",
       "3                       [en]\n",
       "4                   [en, hi]\n",
       "5                       [ko]\n",
       "                ...         \n",
       "7394    [en, de, ja, la, es]\n",
       "7395                    [en]\n",
       "7396                    [en]\n",
       "7397                    [en]\n",
       "7398                    [fr]\n",
       "Name: spoken_languages, Length: 7398, dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['spoken_languages'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[index,'spoken_languages'] = original_language[index].map(lambda x: [x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 增加一列spokens 表示语言的数目\n",
    "data['spokens'] = data['spoken_languages'].map(lambda x: len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['spokens'].map(lambda x: 1 if x==0 else 0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "en\n",
      "fr\n",
      "hi\n",
      "ru\n",
      "es\n",
      "ja\n",
      "it\n",
      "ko\n",
      "de\n",
      "zh\n",
      "cn\n",
      "ta\n",
      "sv\n",
      "da\n",
      "pt\n",
      "ml\n",
      "nl\n"
     ]
    }
   ],
   "source": [
    "# 增加new_cosl\n",
    "for col in new_cosl.index:\n",
    "    print(col)\n",
    "    data[col] = data['original_language'].map(lambda x: 1 if x==col else 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
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       "</table>\n",
       "<p>7398 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      en  fr  hi  ru  es  ja  it  ko  de  zh  cn  ta  sv  da  pt  ml  nl\n",
       "id                                                                      \n",
       "1      1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
       "2      1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
       "3      1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
       "4      0   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
       "5      0   0   0   0   0   0   0   1   0   0   0   0   0   0   0   0   0\n",
       "...   ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..\n",
       "7394   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
       "7395   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
       "7396   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
       "7397   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
       "7398   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
       "\n",
       "[7398 rows x 17 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[new_cosl.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把original_language 删除\n",
    "data.drop(\"original_language\", axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "revenue                 4398\n",
       "Keywords                 669\n",
       "production_companies     414\n",
       "production_countries     157\n",
       "crew                      38\n",
       "cast                      26\n",
       "genres                    23\n",
       "dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_count(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'id': 18, 'name': 'Drama'}]                                                                                                                                          614\n",
       "[{'id': 35, 'name': 'Comedy'}]                                                                                                                                         477\n",
       "[{'id': 18, 'name': 'Drama'}, {'id': 10749, 'name': 'Romance'}]                                                                                                        287\n",
       "[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'name': 'Drama'}]                                                                                                            219\n",
       "[{'id': 35, 'name': 'Comedy'}, {'id': 10749, 'name': 'Romance'}]                                                                                                       212\n",
       "                                                                                                                                                                      ... \n",
       "[{'id': 18, 'name': 'Drama'}, {'id': 99, 'name': 'Documentary'}]                                                                                                         1\n",
       "[{'id': 18, 'name': 'Drama'}, {'id': 16, 'name': 'Animation'}, {'id': 10752, 'name': 'War'}]                                                                             1\n",
       "[{'id': 27, 'name': 'Horror'}, {'id': 878, 'name': 'Science Fiction'}, {'id': 14, 'name': 'Fantasy'}]                                                                    1\n",
       "[{'id': 14, 'name': 'Fantasy'}, {'id': 27, 'name': 'Horror'}, {'id': 28, 'name': 'Action'}, {'id': 53, 'name': 'Thriller'}, {'id': 878, 'name': 'Science Fiction'}]      1\n",
       "[{'id': 18, 'name': 'Drama'}, {'id': 10751, 'name': 'Family'}, {'id': 10749, 'name': 'Romance'}, {'id': 10402, 'name': 'Music'}]                                         1\n",
       "Name: genres, Length: 1520, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['genres'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>original_title</th>\n",
       "      <th>popularity</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>production_countries</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>Keywords</th>\n",
       "      <th>...</th>\n",
       "      <th>ko</th>\n",
       "      <th>de</th>\n",
       "      <th>zh</th>\n",
       "      <th>cn</th>\n",
       "      <th>ta</th>\n",
       "      <th>sv</th>\n",
       "      <th>da</th>\n",
       "      <th>pt</th>\n",
       "      <th>ml</th>\n",
       "      <th>nl</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>2000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0349159</td>\n",
       "      <td>The Book of Mormon Movie, Volume 1: The Journey</td>\n",
       "      <td>0.079856</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>[en]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1623</th>\n",
       "      <td>400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0261755</td>\n",
       "      <td>Jackpot</td>\n",
       "      <td>0.218588</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>[en]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1815</th>\n",
       "      <td>2700000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0110289</td>\n",
       "      <td>Курочка Ряба</td>\n",
       "      <td>0.677253</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'iso_3166_1': 'FR', 'name': 'France'}, {'iso...</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>[ru]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1820</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0352622</td>\n",
       "      <td>Небо. Самолёт. Девушка.</td>\n",
       "      <td>0.518078</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'iso_3166_1': 'RU', 'name': 'Russia'}]</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>[ru]</td>\n",
       "      <td>[{'id': 187056, 'name': 'woman director'}]</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2424</th>\n",
       "      <td>500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0984177</td>\n",
       "      <td>Amarkalam</td>\n",
       "      <td>0.493342</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'iso_3166_1': 'IN', 'name': 'India'}]</td>\n",
       "      <td>157.000000</td>\n",
       "      <td>[ta]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2687</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0833448</td>\n",
       "      <td>Лифт</td>\n",
       "      <td>0.158207</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>[ru]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2901</th>\n",
       "      <td>200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt1766044</td>\n",
       "      <td>Poslednyaya skazka Rity</td>\n",
       "      <td>0.560685</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'iso_3166_1': 'RU', 'name': 'Russia'}]</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>[ru]</td>\n",
       "      <td>[{'id': 187056, 'name': 'woman director'}]</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3074</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0090904</td>\n",
       "      <td>Dangerously Close</td>\n",
       "      <td>0.004425</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>[de, en]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <th>3794</th>\n",
       "      <td>8000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0086405</td>\n",
       "      <td>Table For Five</td>\n",
       "      <td>0.406505</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>[en]</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3911</th>\n",
       "      <td>1300000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0044177</td>\n",
       "      <td>Valentino</td>\n",
       "      <td>0.328342</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>[en]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4222</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0108234</td>\n",
       "      <td>Street Knight</td>\n",
       "      <td>0.001393</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>4443</th>\n",
       "      <td>0</td>\n",
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       "      <td>tt1572916</td>\n",
       "      <td>아들</td>\n",
       "      <td>0.153181</td>\n",
       "      <td>[{'name': 'Cinema Service', 'id': 868}, {'name...</td>\n",
       "      <td>[{'iso_3166_1': 'KR', 'name': 'South Korea'}]</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>[ko]</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4616</th>\n",
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       "      <td>tt1569465</td>\n",
       "      <td>Наша Russia: Яйца судьбы</td>\n",
       "      <td>0.995285</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'iso_3166_1': 'RU', 'name': 'Russia'}]</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>[ru]</td>\n",
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       "      <th>4965</th>\n",
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       "      <td>tt0405699</td>\n",
       "      <td>Антикиллер 2: Антитеррор</td>\n",
       "      <td>0.238166</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'iso_3166_1': 'RU', 'name': 'Russia'}]</td>\n",
       "      <td>125.000000</td>\n",
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       "      <th>5063</th>\n",
       "      <td>0</td>\n",
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       "      <td>15</td>\n",
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       "      <td>[{'name': 'Media Pro Pictures', 'id': 3244}, {...</td>\n",
       "      <td>[{'iso_3166_1': 'RO', 'name': 'Romania'}]</td>\n",
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       "      <td>[ro]</td>\n",
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       "      <td>Duniyadari</td>\n",
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       "      <td>148.000000</td>\n",
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       "      <th>5214</th>\n",
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       "      <td>Praying With Lior</td>\n",
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       "      <td>Teddy Bears' Picnic</td>\n",
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       "      <th>5520</th>\n",
       "      <td>2500000</td>\n",
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       "      <td>tt1620464</td>\n",
       "      <td>Glukhar v kino</td>\n",
       "      <td>0.209434</td>\n",
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       "      <td>[{'iso_3166_1': 'RU', 'name': 'Russia'}]</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>6450</th>\n",
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       "      <td>Lucky Lady</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>118.000000</td>\n",
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       "      <td>Death of a Dynasty</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6565</th>\n",
       "      <td>6000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0361596</td>\n",
       "      <td>Fahrenheit 9/11</td>\n",
       "      <td>6.839460</td>\n",
       "      <td>[{'name': 'BIM Distribuzione', 'id': 225}, {'n...</td>\n",
       "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>[ar, en]</td>\n",
       "      <td>[{'id': 238179, 'name': 'skab under tv'}, {'id...</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6818</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt2192844</td>\n",
       "      <td>Miesten välisiä keskusteluja</td>\n",
       "      <td>0.011427</td>\n",
       "      <td>[{'name': 'Vegetarian Films', 'id': 80999}]</td>\n",
       "      <td>[{'iso_3166_1': 'FI', 'name': 'Finland'}]</td>\n",
       "      <td>107.717262</td>\n",
       "      <td>[fi]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        budget genres    imdb_id  \\\n",
       "id                                 \n",
       "471    2000000    NaN  tt0349159   \n",
       "1623    400000    NaN  tt0261755   \n",
       "1815   2700000    NaN  tt0110289   \n",
       "1820         0    NaN  tt0352622   \n",
       "2424    500000    NaN  tt0984177   \n",
       "2687         0    NaN  tt0833448   \n",
       "2901    200000    NaN  tt1766044   \n",
       "3074         0    NaN  tt0090904   \n",
       "3794   8000000    NaN  tt0086405   \n",
       "3911   1300000    NaN  tt0044177   \n",
       "4222         0    NaN  tt0108234   \n",
       "4443         0    NaN  tt1572916   \n",
       "4616   2000000    NaN  tt1569465   \n",
       "4965         0    NaN  tt0405699   \n",
       "5063         0    NaN  tt0461892   \n",
       "5119    390000    NaN  tt3121604   \n",
       "5214         0    NaN  tt1164092   \n",
       "5252         0    NaN  tt0250282   \n",
       "5520   2500000    NaN  tt1620464   \n",
       "6450  13000000    NaN  tt0073317   \n",
       "6486         0    NaN  tt0361498   \n",
       "6565   6000000    NaN  tt0361596   \n",
       "6818         0    NaN  tt2192844   \n",
       "\n",
       "                                       original_title  popularity  \\\n",
       "id                                                                  \n",
       "471   The Book of Mormon Movie, Volume 1: The Journey    0.079856   \n",
       "1623                                          Jackpot    0.218588   \n",
       "1815                                     Курочка Ряба    0.677253   \n",
       "1820                          Небо. Самолёт. Девушка.    0.518078   \n",
       "2424                                        Amarkalam    0.493342   \n",
       "2687                                             Лифт    0.158207   \n",
       "2901                          Poslednyaya skazka Rity    0.560685   \n",
       "3074                                Dangerously Close    0.004425   \n",
       "3794                                   Table For Five    0.406505   \n",
       "3911                                        Valentino    0.328342   \n",
       "4222                                    Street Knight    0.001393   \n",
       "4443                                               아들    0.153181   \n",
       "4616                         Наша Russia: Яйца судьбы    0.995285   \n",
       "4965                         Антикиллер 2: Антитеррор    0.238166   \n",
       "5063                                               15    0.038560   \n",
       "5119                                       Duniyadari    0.016219   \n",
       "5214                                Praying With Lior    0.004706   \n",
       "5252                              Teddy Bears' Picnic    0.022347   \n",
       "5520                                   Glukhar v kino    0.209434   \n",
       "6450                                       Lucky Lady    0.000657   \n",
       "6486                               Death of a Dynasty    0.902846   \n",
       "6565                                  Fahrenheit 9/11    6.839460   \n",
       "6818                     Miesten välisiä keskusteluja    0.011427   \n",
       "\n",
       "                                   production_companies  \\\n",
       "id                                                        \n",
       "471                                                 NaN   \n",
       "1623                                                NaN   \n",
       "1815                                                NaN   \n",
       "1820                                                NaN   \n",
       "2424                                                NaN   \n",
       "2687                                                NaN   \n",
       "2901                                                NaN   \n",
       "3074                                                NaN   \n",
       "3794                                                NaN   \n",
       "3911                                                NaN   \n",
       "4222                                                NaN   \n",
       "4443  [{'name': 'Cinema Service', 'id': 868}, {'name...   \n",
       "4616                                                NaN   \n",
       "4965                                                NaN   \n",
       "5063  [{'name': 'Media Pro Pictures', 'id': 3244}, {...   \n",
       "5119                                                NaN   \n",
       "5214                                                NaN   \n",
       "5252                                                NaN   \n",
       "5520                                                NaN   \n",
       "6450                                                NaN   \n",
       "6486                                                NaN   \n",
       "6565  [{'name': 'BIM Distribuzione', 'id': 225}, {'n...   \n",
       "6818        [{'name': 'Vegetarian Films', 'id': 80999}]   \n",
       "\n",
       "                                   production_countries     runtime  \\\n",
       "id                                                                    \n",
       "471                                                 NaN  120.000000   \n",
       "1623                                                NaN   97.000000   \n",
       "1815  [{'iso_3166_1': 'FR', 'name': 'France'}, {'iso...  117.000000   \n",
       "1820           [{'iso_3166_1': 'RU', 'name': 'Russia'}]   91.000000   \n",
       "2424            [{'iso_3166_1': 'IN', 'name': 'India'}]  157.000000   \n",
       "2687                                                NaN   88.000000   \n",
       "2901           [{'iso_3166_1': 'RU', 'name': 'Russia'}]  100.000000   \n",
       "3074                                                NaN   95.000000   \n",
       "3794                                                NaN  121.000000   \n",
       "3911  [{'iso_3166_1': 'US', 'name': 'United States o...  102.000000   \n",
       "4222                                                NaN    0.000000   \n",
       "4443      [{'iso_3166_1': 'KR', 'name': 'South Korea'}]  109.000000   \n",
       "4616           [{'iso_3166_1': 'RU', 'name': 'Russia'}]   85.000000   \n",
       "4965           [{'iso_3166_1': 'RU', 'name': 'Russia'}]  125.000000   \n",
       "5063          [{'iso_3166_1': 'RO', 'name': 'Romania'}]   95.000000   \n",
       "5119                                                NaN  148.000000   \n",
       "5214                                                NaN   87.000000   \n",
       "5252                                                NaN   80.000000   \n",
       "5520           [{'iso_3166_1': 'RU', 'name': 'Russia'}]    0.000000   \n",
       "6450                                                NaN  118.000000   \n",
       "6486  [{'iso_3166_1': 'US', 'name': 'United States o...   92.000000   \n",
       "6565  [{'iso_3166_1': 'US', 'name': 'United States o...  122.000000   \n",
       "6818          [{'iso_3166_1': 'FI', 'name': 'Finland'}]  107.717262   \n",
       "\n",
       "     spoken_languages                                           Keywords  ...  \\\n",
       "id                                                                        ...   \n",
       "471              [en]                                                NaN  ...   \n",
       "1623             [en]                                                NaN  ...   \n",
       "1815             [ru]                                                NaN  ...   \n",
       "1820             [ru]         [{'id': 187056, 'name': 'woman director'}]  ...   \n",
       "2424             [ta]                                                NaN  ...   \n",
       "2687             [ru]                                                NaN  ...   \n",
       "2901             [ru]         [{'id': 187056, 'name': 'woman director'}]  ...   \n",
       "3074         [de, en]                                                NaN  ...   \n",
       "3794             [en]                                                NaN  ...   \n",
       "3911             [en]                                                NaN  ...   \n",
       "4222             [en]                                                NaN  ...   \n",
       "4443             [ko]                                                NaN  ...   \n",
       "4616             [ru]                                                NaN  ...   \n",
       "4965             [ru]                                                NaN  ...   \n",
       "5063             [ro]                                                NaN  ...   \n",
       "5119         [mr, hi]                                                NaN  ...   \n",
       "5214             [en]                                                NaN  ...   \n",
       "5252             [en]                                                NaN  ...   \n",
       "5520             [ru]                                                NaN  ...   \n",
       "6450             [en]                                                NaN  ...   \n",
       "6486             [en]                                                NaN  ...   \n",
       "6565         [ar, en]  [{'id': 238179, 'name': 'skab under tv'}, {'id...  ...   \n",
       "6818             [fi]                                                NaN  ...   \n",
       "\n",
       "     ko de  zh  cn  ta  sv  da  pt  ml  nl  \n",
       "id                                          \n",
       "471   0  0   0   0   0   0   0   0   0   0  \n",
       "1623  0  0   0   0   0   0   0   0   0   0  \n",
       "1815  0  0   0   0   0   0   0   0   0   0  \n",
       "1820  0  0   0   0   0   0   0   0   0   0  \n",
       "2424  0  0   0   0   0   0   0   0   0   0  \n",
       "2687  0  0   0   0   0   0   0   0   0   0  \n",
       "2901  0  0   0   0   0   0   0   0   0   0  \n",
       "3074  0  0   0   0   0   0   0   0   0   0  \n",
       "3794  0  0   0   0   0   0   0   0   0   0  \n",
       "3911  0  0   0   0   0   0   0   0   0   0  \n",
       "4222  0  0   0   0   0   0   0   0   0   0  \n",
       "4443  1  0   0   0   0   0   0   0   0   0  \n",
       "4616  0  0   0   0   0   0   0   0   0   0  \n",
       "4965  0  0   0   0   0   0   0   0   0   0  \n",
       "5063  0  0   0   0   0   0   0   0   0   0  \n",
       "5119  0  0   0   0   0   0   0   0   0   0  \n",
       "5214  0  0   0   0   0   0   0   0   0   0  \n",
       "5252  0  0   0   0   0   0   0   0   0   0  \n",
       "5520  0  0   0   0   0   0   0   0   0   0  \n",
       "6450  0  0   0   0   0   0   0   0   0   0  \n",
       "6486  0  0   0   0   0   0   0   0   0   0  \n",
       "6565  0  0   0   0   0   0   0   0   0   0  \n",
       "6818  0  0   0   0   0   0   0   0   0   0  \n",
       "\n",
       "[23 rows x 38 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['genres'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# genres采取和spoken_languages，original_language相似的操作\n",
    "def genres_abstract(x):\n",
    "    temp = eval(x) if isinstance(x, str) else []\n",
    "    ans = []\n",
    "    for i in temp:\n",
    "        ans.append(i['name'])\n",
    "    return ans \n",
    "index = data[data['genres'].isnull()].index # genres为空的index\n",
    "data['genres'] = data['genres'].map(genres_abstract)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['num_genres'] = data['genres'].map(lambda x:len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Comedy': 2605,\n",
       "         'Drama': 3676,\n",
       "         'Family': 675,\n",
       "         'Romance': 1435,\n",
       "         'Thriller': 1869,\n",
       "         'Action': 1735,\n",
       "         'Animation': 382,\n",
       "         'Adventure': 1116,\n",
       "         'Horror': 735,\n",
       "         'Documentary': 221,\n",
       "         'Music': 267,\n",
       "         'Crime': 1084,\n",
       "         'Science Fiction': 744,\n",
       "         'Mystery': 550,\n",
       "         'Foreign': 84,\n",
       "         'Fantasy': 628,\n",
       "         'War': 243,\n",
       "         'Western': 117,\n",
       "         'History': 295,\n",
       "         'TV Movie': 1})"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import collections\n",
    "# 统计电影类型genres的频次\n",
    "counter = collections.Counter()\n",
    "for item in data['genres']:\n",
    "    for i in item:\n",
    "        counter[i]+=1\n",
    "counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 用电影类型做one_hot编码，构造新列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in counter.keys():\n",
    "    data[col] = data['genres'].map(lambda x: 1 if col in x else 0)\n",
    "# geners为空值的全部为0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(\"genres\",axis=1, inplace=True) # 删除genres列"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cast, crew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\n",
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      "\n",
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      "\n"
     ]
    }
   ],
   "source": [
    "# cast：演员的姓名/id/性别，使用json格式\n",
    "\n",
    "# crew：职员（导演/编辑/摄影...）的姓名/id/性别，使用json格式\n",
    "print_feat(data,'cast')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 演员数量越多，说明电影投入越大，增加1列表示电影的演员数量\n",
    "# 这么多演员，不可能全部进行one-hot编码，而且其中有很多小角色的演员对票房影响很小出演机会也少\n",
    "# 因此选择出现次数前30的演员做one-hot编码\n",
    "# 以演员名字做统计\n",
    "# 同时男女演员之间出演电影类型明显有较大差别，动作戏男演员多，情感性女演员多，增加两列分别表示男女演员数量\n",
    "# 对于空值就以全0填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id\n",
       "1    [[Rob Corddry, 2], [Craig Robinson, 2], [Clark...\n",
       "2    [[Anne Hathaway, 1], [Julie Andrews, 1], [H√©c...\n",
       "3    [[Miles Teller, 2], [J.K. Simmons, 2], [Meliss...\n",
       "4    [[Vidya Balan, 1], [Nawazuddin Siddiqui, 2], [...\n",
       "5    [[Kim Kang-woo, 2], [Jo Jae-hyeon, 2], [Park S...\n",
       "Name: cast_info, dtype: object"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 首先从json数据种提取除演员和性别\n",
    "def cast_abstract(x):\n",
    "    temp = eval(x) if isinstance(x, str) else []\n",
    "    ans = []\n",
    "    for item in temp:\n",
    "        ans.append([item['name'], item['gender']])\n",
    "    return ans \n",
    "\n",
    "data['cast_info'] = data['cast'].map(cast_abstract)\n",
    "data['cast_info'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 演员数量列cast_num\n",
    "data['cast_num'] = data['cast_info'].map(lambda x: len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Rob Corddry': 22,\n",
       "         'Craig Robinson': 17,\n",
       "         'Clark Duke': 10,\n",
       "         'Adam Scott': 25,\n",
       "         'Chevy Chase': 16,\n",
       "         'Gillian Jacobs': 11,\n",
       "         'Bianca Haase': 1,\n",
       "         'Collette Wolfe': 9,\n",
       "         'Kumail Nanjiani': 9,\n",
       "         'Kellee Stewart': 3,\n",
       "         'Josh Heald': 3,\n",
       "         'Gretchen Koerner': 4,\n",
       "         'Lisa Loeb': 3,\n",
       "         'Jessica Williams': 4,\n",
       "         'Bruce Buffer': 1,\n",
       "         'Mariana Paola Vicente': 4,\n",
       "         'Christian Slater': 32,\n",
       "         'Jason Jones': 7,\n",
       "         'Olivia Jordan': 4,\n",
       "         'Christine Bently': 3,\n",
       "         'Stacey Asaro': 1,\n",
       "         'John Cusack': 42,\n",
       "         'Adam Herschman': 3,\n",
       "         'Kisha Sierra': 2,\n",
       "         'Anne Hathaway': 22,\n",
       "         'Julie Andrews': 19,\n",
       "         'H√©ctor Elizondo': 22,\n",
       "         'John Rhys-Davies': 15,\n",
       "         'Heather Matarazzo': 7,\n",
       "         'Chris Pine': 19,\n",
       "         'Callum Blue': 3,\n",
       "         'Larry Miller': 19,\n",
       "         'Raven-Symon√©': 7,\n",
       "         'Kathleen Marshall': 3,\n",
       "         'Caroline Goodall': 17,\n",
       "         'Lorraine Nicholson': 4,\n",
       "         'Shannon Wilcox': 6,\n",
       "         'Greg Lewis': 5,\n",
       "         'Abigail Breslin': 16,\n",
       "         'Paul Vogt': 3,\n",
       "         'Joseph Leo Bwarie': 5,\n",
       "         'Hope Alexander-Willis': 2,\n",
       "         'Rowan Joseph': 5,\n",
       "         'Jeffrey Scott Jensen': 1,\n",
       "         'Miles Teller': 14,\n",
       "         'J.K. Simmons': 50,\n",
       "         'Melissa Benoist': 5,\n",
       "         'Austin Stowell': 7,\n",
       "         'Jayson Blair': 2,\n",
       "         'Kavita Patil': 1,\n",
       "         'Paul Reiser': 10,\n",
       "         'Nate Lang': 1,\n",
       "         'Chris Mulkey': 14,\n",
       "         'Damon Gupton': 4,\n",
       "         'Suanne Spoke': 1,\n",
       "         'Max Kasch': 5,\n",
       "         'Charlie Ian': 1,\n",
       "         'Kofi Siriboe': 3,\n",
       "         'C.J. Vana': 1,\n",
       "         'Tarik Lowe': 1,\n",
       "         'Tyler Kimball': 1,\n",
       "         'Rogelio Douglas Jr.': 2,\n",
       "         'Adrian Burks': 1,\n",
       "         'Joseph Bruno': 1,\n",
       "         'Michael D. Cohen': 1,\n",
       "         'Jocelyn Ayanna': 2,\n",
       "         'Keenan Henson': 1,\n",
       "         'Janet Hoskins': 1,\n",
       "         'April Grace': 7,\n",
       "         \"Clifton 'Fou Fou' Eddie\": 1,\n",
       "         'Calvin C. Winbush': 1,\n",
       "         'Marcus Henderson': 5,\n",
       "         'Tony Baker': 1,\n",
       "         'Henry G. Sanders': 11,\n",
       "         'Sam Campisi': 1,\n",
       "         'Jimmie Kirkpatrick': 1,\n",
       "         'Keenan Allen': 1,\n",
       "         'Ayinde Vaughan': 1,\n",
       "         'Shai Golan': 1,\n",
       "         'Yancey Wells': 1,\n",
       "         'Candace Roberge': 1,\n",
       "         'Krista Kilber': 1,\n",
       "         'Cici Leah Campbell': 3,\n",
       "         'Damien Coates': 1,\n",
       "         'Kyle Julian Graham': 2,\n",
       "         'Ellee Jane Hounsell': 1,\n",
       "         'Stephen Hsu': 1,\n",
       "         'Herman Johansen': 1,\n",
       "         'Wendee Lee': 1,\n",
       "         'Dakota Lupo': 2,\n",
       "         'Jesse Mitchell': 1,\n",
       "         'Amanda Newman': 1,\n",
       "         'Joseph Oliveira': 9,\n",
       "         'Michelle Ruff': 1,\n",
       "         'Daniel Weidlein': 1,\n",
       "         'Vidya Balan': 5,\n",
       "         'Nawazuddin Siddiqui': 11,\n",
       "         'Parambrata Chatterjee': 1,\n",
       "         'Saswata Chatterjee': 1,\n",
       "         'Indraneil Sengupta': 1,\n",
       "         'Darshan Jariwala': 5,\n",
       "         'Masood Akhtar': 4,\n",
       "         'Kim Kang-woo': 2,\n",
       "         'Jo Jae-hyeon': 3,\n",
       "         'Park Si-yeon': 2,\n",
       "         'Kim Joon-bae': 2,\n",
       "         'Scott Grimes': 7,\n",
       "         'Tom Bosley': 2,\n",
       "         'Rickie Lee Jones': 1,\n",
       "         'James Earl Jones': 35,\n",
       "         'Jeffrey Dean Morgan': 15,\n",
       "         'Natasha Calis': 1,\n",
       "         'Madison Davenport': 5,\n",
       "         'Kyra Sedgwick': 14,\n",
       "         'Rob LaBelle': 7,\n",
       "         'Nana Gbewonyo ': 1,\n",
       "         'Jim Thorburn ': 1,\n",
       "         'Matisyahu ': 1,\n",
       "         'Quinn Lord': 2,\n",
       "         'Jay Brazeau': 11,\n",
       "         'Erin Simms': 1,\n",
       "         'John Cassini': 10,\n",
       "         'Grant Show': 1,\n",
       "         'Tim Perez': 7,\n",
       "         'Samir Khader': 1,\n",
       "         'Josh Rushing': 1,\n",
       "         'George W. Bush': 7,\n",
       "         'Hassan Ibrahim': 1,\n",
       "         'Tim Curry': 22,\n",
       "         'Kevin Bishop': 5,\n",
       "         'Jennifer Saunders': 7,\n",
       "         'Billy Connolly': 19,\n",
       "         'Dave Goelz': 9,\n",
       "         'Frank Oz': 17,\n",
       "         'Steve Whitmire': 6,\n",
       "         'David Nicholls': 1,\n",
       "         'Frederick Warder': 3,\n",
       "         'Harry Jones': 2,\n",
       "         'Peter Geeves': 2,\n",
       "         'Jessica Hamilton': 1,\n",
       "         'Bob Balaban': 23,\n",
       "         'Christopher Guest': 11,\n",
       "         'John Michael Higgins': 25,\n",
       "         'Eugene Levy': 27,\n",
       "         'Jane Lynch': 24,\n",
       "         'Michael McKean': 27,\n",
       "         \"Catherine O'Hara\": 20,\n",
       "         'Parker Posey': 19,\n",
       "         'Harry Shearer': 19,\n",
       "         'Fred Willard': 22,\n",
       "         'Ed Begley Jr.': 21,\n",
       "         'Rachael Harris': 20,\n",
       "         'Jennifer Coolidge': 24,\n",
       "         'Paul Dooley': 19,\n",
       "         'Don Lake': 8,\n",
       "         'Deborah Theaker': 1,\n",
       "         'Bill Cobbs': 16,\n",
       "         'Michael Mantell': 9,\n",
       "         'Michael Hancock': 1,\n",
       "         'Sylvester Stallone': 54,\n",
       "         'Talia Shire': 12,\n",
       "         'Burt Young': 18,\n",
       "         'Carl Weathers': 12,\n",
       "         'Burgess Meredith': 10,\n",
       "         'Thayer David': 4,\n",
       "         'Joe Spinell': 17,\n",
       "         'Tony Burton': 9,\n",
       "         'Joe Frazier': 1,\n",
       "         'Jimmy Gambina': 2,\n",
       "         'Jodi Letizia': 1,\n",
       "         'Stan Shaw': 8,\n",
       "         'Michael Dorn': 9,\n",
       "         'DeForest Covan': 1,\n",
       "         'Bill Baldwin': 5,\n",
       "         'Lloyd Kaufman': 8,\n",
       "         'Al Silvani': 3,\n",
       "         'George Memmoli': 3,\n",
       "         'Diana Lewis': 1,\n",
       "         \"George O'Hanlon\": 1,\n",
       "         'Larry Carroll': 1,\n",
       "         'Frank Stallone': 5,\n",
       "         'Don Sherman': 2,\n",
       "         'Billy Sands': 2,\n",
       "         'Simmy Bow': 5,\n",
       "         'Jane Marla Robbins': 3,\n",
       "         'Frank Pesce': 12,\n",
       "         'John Pleshette': 3,\n",
       "         'Lavelle Roby': 5,\n",
       "         'Pedro Lovell': 2,\n",
       "         'Stu Nahan': 3,\n",
       "         'Hank Rolike': 1,\n",
       "         \"Shirley O'Hara\": 1,\n",
       "         'Kathleen Parker': 1,\n",
       "         'Frankie Van': 2,\n",
       "         'Arnold Johnson': 3,\n",
       "         'Arthur Tovey': 13,\n",
       "         'Robert Carradine': 16,\n",
       "         'Curtis Armstrong': 12,\n",
       "         'Larry B. Scott': 2,\n",
       "         'Timothy Busfield': 4,\n",
       "         'Courtney Thorne-Smith': 3,\n",
       "         'Andrew Cassese': 1,\n",
       "         'Donald Gibb': 4,\n",
       "         'Bradley Whitford': 22,\n",
       "         'Ed Lauter': 22,\n",
       "         'Barry Sobel': 3,\n",
       "         'Anthony Edwards': 17,\n",
       "         'James Hong': 28,\n",
       "         'Ruben Rabasa': 6,\n",
       "         'James Cromwell': 32,\n",
       "         'Kevin Spacey': 35,\n",
       "         'Annette Bening': 19,\n",
       "         'Thora Birch': 10,\n",
       "         'Wes Bentley': 14,\n",
       "         'Mena Suvari': 15,\n",
       "         'Chris Cooper': 31,\n",
       "         'Scott Bakula': 6,\n",
       "         'Peter Gallagher': 17,\n",
       "         'Allison Janney': 34,\n",
       "         'Sam Robards': 7,\n",
       "         'Barry Del Sherman': 4,\n",
       "         'John Cho': 20,\n",
       "         'Hal Fort Atkinson': 2,\n",
       "         'Kent Faulcon': 4,\n",
       "         'Ara Celi': 2,\n",
       "         'Sue Casey': 2,\n",
       "         'Brenda Wehle': 5,\n",
       "         'Lisa Cloud': 1,\n",
       "         'Amber Smith': 5,\n",
       "         'Joel McCrary': 6,\n",
       "         'Marissa Jaret Winokur': 3,\n",
       "         'Dennis Anderson': 1,\n",
       "         'Matthew Kimbrough': 5,\n",
       "         'Erin Cathryn Strubbe': 1,\n",
       "         'Alison Faulk': 4,\n",
       "         'Krista Goodsitt': 1,\n",
       "         'Lily Houtkin': 1,\n",
       "         'Carolina Lancaster': 2,\n",
       "         'Mona Leah': 1,\n",
       "         'Chekesha Van Putten': 1,\n",
       "         'Emily Zachary': 1,\n",
       "         'Nancy Anderson': 3,\n",
       "         'Reshma Gajjar': 3,\n",
       "         'Stephanie Rizzo': 1,\n",
       "         'Heather Joy Sher': 1,\n",
       "         'Chelsea Hertford': 1,\n",
       "         'Elaine Corral Kendall': 2,\n",
       "         'David C. Fisher': 4,\n",
       "         'Tom Miller': 1,\n",
       "         'Bruce Cohen': 1,\n",
       "         'John Travolta': 37,\n",
       "         'Uma Thurman': 25,\n",
       "         'Vince Vaughn': 34,\n",
       "         'Cedric the Entertainer': 23,\n",
       "         'Andr√© Benjamin': 7,\n",
       "         'Steven Tyler': 4,\n",
       "         'Robert Pastorelli': 8,\n",
       "         'Christina Milian': 5,\n",
       "         'Paul Adelstein': 6,\n",
       "         'Debi Mazar': 21,\n",
       "         'Gregory Alan Williams': 10,\n",
       "         'Harvey Keitel': 35,\n",
       "         'Dwayne Johnson': 32,\n",
       "         'Danny DeVito': 36,\n",
       "         'James Woods': 28,\n",
       "         'Anthony J. Ribustello': 2,\n",
       "         'Wyclef Jean': 2,\n",
       "         'Fred Durst': 2,\n",
       "         'S√©rgio Mendes': 1,\n",
       "         'Gene Simmons': 5,\n",
       "         'RZA': 14,\n",
       "         'Joe Perry': 2,\n",
       "         'Anna Nicole Smith': 2,\n",
       "         'Alex Kubik': 1,\n",
       "         'Darren Carter': 1,\n",
       "         'Carol Duboc': 1,\n",
       "         'Minae Noji': 4,\n",
       "         'Arielle Kebbel': 8,\n",
       "         'Kimberly J. Brown': 3,\n",
       "         'Margaret Travolta': 6,\n",
       "         'Scott Adsit': 12,\n",
       "         'Brian Christensen': 2,\n",
       "         'Nick Loren': 5,\n",
       "         'Craig Susser': 4,\n",
       "         'George Fisher': 6,\n",
       "         'Sahar Simmons': 1,\n",
       "         'Serdar Kalsin': 3,\n",
       "         'Russ Irwin': 1,\n",
       "         'Joey Kramer': 1,\n",
       "         'Brad Whitford': 1,\n",
       "         'Tom Hamilton': 1,\n",
       "         'Joyce Tolbert': 1,\n",
       "         'Noelle Scaggs': 1,\n",
       "         'Will.i.am': 6,\n",
       "         'Fergie': 5,\n",
       "         'Apl.de.Ap ': 1,\n",
       "         'Taboo': 2,\n",
       "         'Carmen Getit': 1,\n",
       "         'Steve Lucky': 1,\n",
       "         'Kimberly Wyatt': 3,\n",
       "         'Kasey Campbell': 2,\n",
       "         'Ashley Roberts': 1,\n",
       "         'Nicole Scherzinger': 3,\n",
       "         'Shanell Woodgett': 1,\n",
       "         'Clifford McGhee': 1,\n",
       "         'Christopher Toler': 2,\n",
       "         \"Ivan 'Flipz' Velez\": 3,\n",
       "         'Donyelle Denise Jones': 1,\n",
       "         'Gustavo Vargas': 4,\n",
       "         'Tom Cruise': 41,\n",
       "         'Colin Farrell': 33,\n",
       "         'Samantha Morton': 9,\n",
       "         'Max von Sydow': 27,\n",
       "         'Lois Smith': 16,\n",
       "         'Peter Stormare': 31,\n",
       "         'Tim Blake Nelson': 25,\n",
       "         'Steve Harris': 8,\n",
       "         'Kathryn Morris': 7,\n",
       "         'Mike Binder': 2,\n",
       "         'Daniel London': 5,\n",
       "         'Neal McDonough': 17,\n",
       "         'Jessica Capshaw': 2,\n",
       "         'Patrick Kilpatrick': 7,\n",
       "         'Jessica Harper': 4,\n",
       "         'Ashley Crow': 3,\n",
       "         'Arye Gross': 6,\n",
       "         'Fiona Hale': 4,\n",
       "         'George Wallace': 13,\n",
       "         'Frank Grillo': 18,\n",
       "         'Cameron Diaz': 35,\n",
       "         'William Mapother': 10,\n",
       "         'Jason Antoon': 5,\n",
       "         'Nikola Rakoƒçeviƒá': 1,\n",
       "         'Viktor Saviƒá': 1,\n",
       "         'Nata≈°a Tapu≈°koviƒá': 1,\n",
       "         'Nikola Kojo': 2,\n",
       "         'Bojana Novakoviƒá': 4,\n",
       "         'Dragan Miƒáanoviƒá': 4,\n",
       "         'Srƒëan Miletiƒá': 1,\n",
       "         'Predrag Ejdus': 1,\n",
       "         \"Milan 'Caci' Mihailoviƒá\": 1,\n",
       "         'Branislav Jevic': 1,\n",
       "         'Jasmina Avramoviƒá': 1,\n",
       "         'Dimitrije Vojnov': 1,\n",
       "         'Nikola Gli≈°iƒá': 1,\n",
       "         'Rachel McAdams': 21,\n",
       "         'Cillian Murphy': 18,\n",
       "         'Brian Cox': 38,\n",
       "         'Jayma Mays': 5,\n",
       "         'Jack Scalia': 1,\n",
       "         'Robert Pine': 6,\n",
       "         'Terry Press': 1,\n",
       "         'Brittany Oaks': 1,\n",
       "         'Laura Johnson': 2,\n",
       "         'Kyle Gallner': 12,\n",
       "         'Angela Paton': 8,\n",
       "         'Loren Lester': 3,\n",
       "         'Suzie Plakson': 3,\n",
       "         'Monica McSwain': 2,\n",
       "         'Beth Toussaint': 1,\n",
       "         'Adam Gobble': 1,\n",
       "         'Megan Crawford': 1,\n",
       "         'Carl Gilliard': 2,\n",
       "         'Mary Kathleen Gordon': 1,\n",
       "         'Philip Pavel': 6,\n",
       "         'Amber Mead': 4,\n",
       "         'Dey Young': 4,\n",
       "         'Jeanine Jackson': 9,\n",
       "         'Ralph Fiennes': 31,\n",
       "         'Felicity Jones': 15,\n",
       "         'Joanna Scanlan': 7,\n",
       "         'Kristin Scott Thomas': 21,\n",
       "         'Tom Hollander': 19,\n",
       "         'Michelle Fairley': 6,\n",
       "         'John Kavanagh': 5,\n",
       "         'Amanda Hale': 2,\n",
       "         'Perdita Weeks': 3,\n",
       "         'Tom Burke': 3,\n",
       "         'Richard McCabe': 6,\n",
       "         'David Collings': 1,\n",
       "         'James Michael Rankin': 1,\n",
       "         'Charlotte Hope': 4,\n",
       "         'Michael Marcus': 4,\n",
       "         'Laurence Spellman': 1,\n",
       "         'Jonathan Harden': 1,\n",
       "         'Christos Lawton': 2,\n",
       "         'Claire Daly': 1,\n",
       "         'Ed Westwick': 8,\n",
       "         'Brooke Shields': 12,\n",
       "         'Bill Nighy': 34,\n",
       "         'Tamsin Egerton': 5,\n",
       "         'Bill Bailey': 9,\n",
       "         'Sophia Bush': 5,\n",
       "         'Nicholas Braun': 11,\n",
       "         'Georgia King': 6,\n",
       "         'Tom Goodman-Hill': 5,\n",
       "         'Gregor Blo√©b': 2,\n",
       "         'Adam Bousdoukos': 3,\n",
       "         'Ken Duken': 4,\n",
       "         'Jo Martin': 2,\n",
       "         'Rebecca Lacey': 1,\n",
       "         'Chandra Ruegg': 1,\n",
       "         'Alex MacQueen': 8,\n",
       "         'Mike Goodenough': 1,\n",
       "         'Steve Furst': 1,\n",
       "         'Graham Lee': 1,\n",
       "         'Tara Dakides': 1,\n",
       "         'Abbie Dunn': 2,\n",
       "         'Patrick Finger': 1,\n",
       "         'Jason Statham': 33,\n",
       "         'Amber Valletta': 9,\n",
       "         'Kate Nauta': 2,\n",
       "         'Alessandro Gassman': 1,\n",
       "         'Fran√ßois Berl√©and': 5,\n",
       "         'Robert Small': 4,\n",
       "         'Matthew Modine': 15,\n",
       "         'Jason Flemyng': 28,\n",
       "         'Keith David': 44,\n",
       "         'Ron Madoff': 6,\n",
       "         'Gary Oldman': 35,\n",
       "         'William Hurt': 28,\n",
       "         'Matt LeBlanc': 4,\n",
       "         'Mimi Rogers': 13,\n",
       "         'Heather Graham': 22,\n",
       "         'Lacey Chabert': 7,\n",
       "         'Jack Johnson': 2,\n",
       "         'Jared Harris': 29,\n",
       "         'Mark Goddard': 1,\n",
       "         'Lennie James': 8,\n",
       "         'Marta Kristen': 1,\n",
       "         'June Lockhart': 6,\n",
       "         'Adam Sims': 1,\n",
       "         'Angela Cartwright': 3,\n",
       "         'John Sharian': 7,\n",
       "         'Abigail Canton': 2,\n",
       "         'Richard Saperstein': 1,\n",
       "         'Dick Tufeld': 1,\n",
       "         'Gary A. Hecker': 5,\n",
       "         'Edward Fox': 8,\n",
       "         'Dana Kimmell': 2,\n",
       "         'Paul Kratka': 1,\n",
       "         'Tracie Savage': 1,\n",
       "         'Jeffrey Rogers': 1,\n",
       "         'Catherine Parks': 2,\n",
       "         'Larry Zerner': 1,\n",
       "         'Rachel Howard': 1,\n",
       "         'David Katims': 1,\n",
       "         'Nick Savage': 2,\n",
       "         'Gloria Charles': 1,\n",
       "         \"Kevin O'Brien\": 2,\n",
       "         'Richard Brooker': 2,\n",
       "         'Steve Susskind': 1,\n",
       "         'David Wiley': 1,\n",
       "         'Perla Walter': 2,\n",
       "         'Anne Gaybis': 4,\n",
       "         'Calvin Lee Reeder': 2,\n",
       "         'Lane Hughes': 2,\n",
       "         'Adam Wingard': 2,\n",
       "         'Hannah Fierman': 1,\n",
       "         'Mike Donlan': 1,\n",
       "         'Joe Sykes': 1,\n",
       "         'Drew Sawyer': 1,\n",
       "         'Jas Sams': 1,\n",
       "         'Joe Swanberg': 5,\n",
       "         'Sophia Takal': 2,\n",
       "         'Kate Lyn Sheil': 5,\n",
       "         'Drew Moerlein': 1,\n",
       "         'Jason Yachanin': 1,\n",
       "         'Helen Rogers': 2,\n",
       "         'Chad Villella': 1,\n",
       "         'Matt Bettinelli-Olpin': 2,\n",
       "         'Tyler Gillett': 1,\n",
       "         'Paul Natonek': 1,\n",
       "         'Nicholas Tecosky': 1,\n",
       "         'Nicole Erb': 1,\n",
       "         'John Walcutt': 4,\n",
       "         'Bilal Mir': 1,\n",
       "         'Damion Stephens': 1,\n",
       "         'Koz McRae': 1,\n",
       "         'Eric Curtis': 1,\n",
       "         'Nicole Boccumini': 1,\n",
       "         'Lisa Marie Thomas': 3,\n",
       "         'Melinda Fleming': 1,\n",
       "         'Rob Mosca': 1,\n",
       "         'Kentucker Audley': 3,\n",
       "         'Max Perlich': 8,\n",
       "         'Lesley-Ann Brandt': 1,\n",
       "         'Matt Knudsen': 2,\n",
       "         'Rick Overton': 15,\n",
       "         'Rance Howard': 17,\n",
       "         'Jamie Wozny': 1,\n",
       "         'Lonnie Henderson': 1,\n",
       "         'Brent Pope': 1,\n",
       "         'Tim Abell': 2,\n",
       "         'Melissa McCarty': 1,\n",
       "         'Daniel Roebuck': 11,\n",
       "         'Sean Patrick Flanery': 8,\n",
       "         'Natalie Zea': 3,\n",
       "         'Angeline-Rose Troy': 1,\n",
       "         'Adam Baldwin': 13,\n",
       "         'Thomas Ian Nicholas': 8,\n",
       "         'Veronica Cartwright': 13,\n",
       "         'Christopher Lloyd': 30,\n",
       "         'Juliet Landau': 2,\n",
       "         'Nathan Meister': 3,\n",
       "         'Peter Feeney': 2,\n",
       "         'Danielle Mason': 1,\n",
       "         'James Ashcroft': 3,\n",
       "         'Mick Rose': 1,\n",
       "         'Tammy Davis': 2,\n",
       "         'Glenis Levestam': 2,\n",
       "         'Tandi Wright': 2,\n",
       "         'Matthew Chamberlain': 2,\n",
       "         'Oliver Driver': 1,\n",
       "         'Nick Fenton': 1,\n",
       "         'Min Windle': 2,\n",
       "         'Ian Harcourt': 6,\n",
       "         'Larry Drake': 4,\n",
       "         'Holly Marie Combs': 5,\n",
       "         'Cliff DeYoung': 10,\n",
       "         'Glenn Quinn': 2,\n",
       "         'Keith Diamond': 3,\n",
       "         'Richard Bradford': 16,\n",
       "         'Michelle Johnson': 7,\n",
       "         'Nancy Fish': 8,\n",
       "         'Sara Melson': 1,\n",
       "         'Zoe Trilling': 1,\n",
       "         'Darin Heames': 4,\n",
       "         'Deborah Tucker': 1,\n",
       "         'Doug E. Doug': 3,\n",
       "         'Denise Barnes': 1,\n",
       "         'John Vickery': 2,\n",
       "         'Steve Martin': 30,\n",
       "         'Campbell Scott': 11,\n",
       "         'Ben Gazzara': 10,\n",
       "         'Rebecca Pidgeon': 4,\n",
       "         'Ricky Jay': 10,\n",
       "         'Felicity Huffman': 8,\n",
       "         'Daniel Radcliffe': 17,\n",
       "         'Zoe Kazan': 12,\n",
       "         'Rafe Spall': 14,\n",
       "         'Megan Park': 3,\n",
       "         'Adam Driver': 13,\n",
       "         'Jemima Rooper': 5,\n",
       "         'Meghan Heffern': 2,\n",
       "         'Jordan Hayes': 2,\n",
       "         'Mackenzie Davis': 6,\n",
       "         'Vanessa Matsui': 2,\n",
       "         'Sarah Gadon': 11,\n",
       "         'Lucius Hoyos': 1,\n",
       "         'Tommie-Amber Pirie': 1,\n",
       "         'Jonathan Cherry': 4,\n",
       "         'Rebecca Northan': 2,\n",
       "         'Oona Chaplin': 5,\n",
       "         'Adam Fergus': 1,\n",
       "         'Sam Moses': 4,\n",
       "         'Ennis Esmer': 1,\n",
       "         'Mike Wilmot': 3,\n",
       "         'George Tchortov': 2,\n",
       "         'Tamara Duarte': 1,\n",
       "         'Rosalind Feldman': 1,\n",
       "         'Don Ritter': 1,\n",
       "         'Judd Nelson': 9,\n",
       "         'Peter Cullen': 8,\n",
       "         'Frank Welker': 40,\n",
       "         'Leonard Nimoy': 12,\n",
       "         'Orson Welles': 12,\n",
       "         'Eric Idle': 15,\n",
       "         'Robert Stack': 6,\n",
       "         'Norman Alden': 5,\n",
       "         'Jack Angel': 9,\n",
       "         'Michael Bell': 5,\n",
       "         'Gregg Berger': 3,\n",
       "         'Susan Blu': 2,\n",
       "         'Lionel Stander': 3,\n",
       "         'John Moschitta, Jr.': 1,\n",
       "         'Buster Jones': 1,\n",
       "         'Paul Eiding': 6,\n",
       "         'Neil Ross': 6,\n",
       "         'Scatman Crothers': 10,\n",
       "         'Casey Kasem': 4,\n",
       "         'Dan Gilvezan': 1,\n",
       "         'Corey Burton': 16,\n",
       "         'Roger C. Carmel': 4,\n",
       "         'Stanley Jones': 1,\n",
       "         'Chris Latta': 1,\n",
       "         'Arthur Burghardt': 2,\n",
       "         'Don Messick': 2,\n",
       "         'Ed Gilbert': 4,\n",
       "         'Clive Revill': 3,\n",
       "         'Hal Rayle': 1,\n",
       "         'David Mendenhall': 2,\n",
       "         'Victor Caroli': 1,\n",
       "         'Ben Affleck': 42,\n",
       "         'Samuel L. Jackson': 80,\n",
       "         'Kim Staunton': 3,\n",
       "         'Toni Collette': 25,\n",
       "         'Sydney Pollack': 9,\n",
       "         'Matt Malloy': 14,\n",
       "         'John Benjamin Hickey': 11,\n",
       "         'Amanda Peet': 25,\n",
       "         'Michael Pitt': 16,\n",
       "         'Myra Lucretia Taylor': 5,\n",
       "         'Fran√ßois Cluzet': 5,\n",
       "         'Omar Sy': 11,\n",
       "         'Audrey Fleurot': 4,\n",
       "         'Anne Le Ny': 1,\n",
       "         'Clotilde Mollet': 4,\n",
       "         'Alba Ga√Øa Kraghede Bellugi': 1,\n",
       "         'Cyril Mendy': 2,\n",
       "         'Christian Ameri': 2,\n",
       "         'Marie-Laure Descoureaux': 2,\n",
       "         'Salimata Kamate': 1,\n",
       "         'Absa Diatou Toure': 1,\n",
       "         'Dominique Daguier': 1,\n",
       "         'Fran√ßois Caron': 1,\n",
       "         'Thomas Soliv√©res': 1,\n",
       "         'Gr√©goire Oestermann': 2,\n",
       "         'Doroth√©e Bri√®re': 2,\n",
       "         'Jos√©phine de Meaux': 3,\n",
       "         'Emilie Caen': 1,\n",
       "         'Caroline Bourg': 1,\n",
       "         'Sylvain Lazard': 1,\n",
       "         'Jean-Fran√ßois Cayrey': 1,\n",
       "         'Ian Fenelon': 1,\n",
       "         'Renaud Barse': 2,\n",
       "         'Fran√ßois Bureloup': 3,\n",
       "         'Nicky Marbot': 1,\n",
       "         'Benjamin Baroche': 2,\n",
       "         'J√©r√¥me Pauwels': 1,\n",
       "         'Antoine Laurent': 2,\n",
       "         'Fabrice Mantegna': 1,\n",
       "         'Hedi Bouchenafa': 1,\n",
       "         'Michel Winogradoff': 3,\n",
       "         'Elliot Latil': 1,\n",
       "         'Daniel Auteuil': 4,\n",
       "         'Juliette Binoche': 22,\n",
       "         'Annie Girardot': 2,\n",
       "         'Bernard Le Coq': 3,\n",
       "         'Daniel Duval': 2,\n",
       "         'Maurice B√©nichou': 4,\n",
       "         'Walid Afkir': 3,\n",
       "         'Lester Makedonsky': 1,\n",
       "         'Nathalie Richard': 5,\n",
       "         'Denis Podalyd√®s': 1,\n",
       "         'Caroline Baehr': 1,\n",
       "         'Christian Benedetti': 1,\n",
       "         'Lo√Øc Brabant': 2,\n",
       "         'A√Øssa Ma√Øga': 5,\n",
       "         'Vin Diesel': 24,\n",
       "         'Rose Leslie': 3,\n",
       "         'Elijah Wood': 30,\n",
       "         '√ìlafur Darri √ìlafsson': 6,\n",
       "         'Rena Owen': 5,\n",
       "         'Julie Engelbrecht': 1,\n",
       "         'Michael Caine': 40,\n",
       "         'Joseph Gilgun': 6,\n",
       "         'Isaach De Bankol√©': 10,\n",
       "         'Sloane Coombs': 1,\n",
       "         'Dawn Olivieri': 2,\n",
       "         'Inbar Lavi': 1,\n",
       "         'Bex Taylor-Klaus': 1,\n",
       "         'Aimee Carrero': 2,\n",
       "         'Armani Jackson': 2,\n",
       "         'Samara Lee': 3,\n",
       "         'Stephanie Bertoni': 1,\n",
       "         'Kurt Angle': 3,\n",
       "         'David Whalen': 3,\n",
       "         'Jack Erdie': 4,\n",
       "         'Toussaint Raphael Abessolo': 1,\n",
       "         'Allegra Carpenter': 3,\n",
       "         'Eric Jacobus': 1,\n",
       "         'Julian Barratt': 3,\n",
       "         'Ian Virgo': 1,\n",
       "         'Miguel √Ångel Mu√±oz': 1,\n",
       "         'Dana Meinrath': 1,\n",
       "         'Richard Hardisty': 1,\n",
       "         'Sherry Lara': 1,\n",
       "         'Francisco Barreiro': 2,\n",
       "         'Julija Steponaitytƒó': 1,\n",
       "         'Michael Isokpan': 1,\n",
       "         'Ehigiator Joy Nosa': 1,\n",
       "         'Patrick Daniel': 2,\n",
       "         'Lauren Molina': 2,\n",
       "         'Michael Dragon Vincent': 1,\n",
       "         'Aki Morita': 1,\n",
       "         'Bryan Connolly': 1,\n",
       "         'Jordan D. Morris': 1,\n",
       "         'Jess Lane': 1,\n",
       "         'Andr√© Hennicke': 5,\n",
       "         'Victoria Broom': 1,\n",
       "         'Alan McKenna': 3,\n",
       "         'Tristan Risk': 1,\n",
       "         'James McDougall': 1,\n",
       "         'Mark Grossman': 1,\n",
       "         'Jason Edmiston': 1,\n",
       "         'Jano Badovinac': 1,\n",
       "         'B√©atrice Dalle': 3,\n",
       "         'Tess Maury': 1,\n",
       "         'Sakurako Mizuki': 1,\n",
       "         'Delphine Roussel': 1,\n",
       "         'Nicholas Amer': 3,\n",
       "         'Chris Tucker': 8,\n",
       "         'Jackie Chan': 32,\n",
       "         'Hiroyuki Sanada': 12,\n",
       "         'Yvan Attal': 3,\n",
       "         'Roman Polanski': 3,\n",
       "         'Zhang Jingchu': 2,\n",
       "         'Philip Baker Hall': 31,\n",
       "         'No√©mie Lenoir': 4,\n",
       "         'Sarah Shahi': 5,\n",
       "         'Youki Kudoh': 2,\n",
       "         'Tzi Ma': 11,\n",
       "         'Dana Ivey': 14,\n",
       "         'Henry O': 5,\n",
       "         'Mia Tyler': 1,\n",
       "         'Michael Chow': 4,\n",
       "         'Kentaro': 3,\n",
       "         'Ann Christine': 2,\n",
       "         'Chris Sarandon': 10,\n",
       "         'Catherine Chan': 1,\n",
       "         'Robert John Burke': 15,\n",
       "         'Anson Mount': 9,\n",
       "         'Reggie Lee': 8,\n",
       "         'Joseph Sikora': 4,\n",
       "         'Igor Jijikine': 6,\n",
       "         'Elissa Middleton': 1,\n",
       "         'Jack Gwaltney': 3,\n",
       "         'Barry Bradford': 1,\n",
       "         'Jay Giannone': 3,\n",
       "         'Laurence Covington': 1,\n",
       "         \"Matt O'Toole\": 3,\n",
       "         'Kate Hudson': 23,\n",
       "         \"Frances O'Connor\": 9,\n",
       "         'Tommy Tiernan': 1,\n",
       "         'Stuart Townsend': 6,\n",
       "         'Cathleen Bradley': 1,\n",
       "         'Brendan Dempsey': 2,\n",
       "         'Charlotte Bradley': 2,\n",
       "         'Rosaleen Linehan': 1,\n",
       "         'Alan Maher': 1,\n",
       "         'Kathy Downes': 1,\n",
       "         'Donal Beecher': 1,\n",
       "         'Roger Gregg': 1,\n",
       "         'Stewart Roche': 1,\n",
       "         'Aoife Maloney': 1,\n",
       "         'Paul Cotrulia': 1,\n",
       "         'Dustin Hoffman': 43,\n",
       "         'Rene Russo': 19,\n",
       "         'Morgan Freeman': 61,\n",
       "         'Cuba Gooding Jr.': 25,\n",
       "         'Donald Sutherland': 39,\n",
       "         'Patrick Dempsey': 16,\n",
       "         'Benito Martinez': 6,\n",
       "         'Malick Bowens': 8,\n",
       "         'Zakes Mokae': 7,\n",
       "         'Bruce Jarchow': 4,\n",
       "         'Dale Dye': 16,\n",
       "         'Conrad Bachmann': 3,\n",
       "         'Susan Lee Hoffman': 2,\n",
       "         'Jah Shams': 1,\n",
       "         'Mary Grace': 1,\n",
       "         'Masashi Nagadoi': 2,\n",
       "         'Dave Edwards': 1,\n",
       "         'Jon Briddell': 1,\n",
       "         'Charlton Heston': 24,\n",
       "         'Richard Harris': 16,\n",
       "         'Jim Hutton': 2,\n",
       "         'James Coburn': 17,\n",
       "         'Michael Anderson Jr.': 3,\n",
       "         'Senta Berger': 1,\n",
       "         'Mario Adorf': 1,\n",
       "         'Brock Peters': 5,\n",
       "         'Warren Oates': 7,\n",
       "         'Ben Johnson': 15,\n",
       "         'R. G. Armstrong': 12,\n",
       "         'Slim Pickens': 10,\n",
       "         'Karl Swenson': 6,\n",
       "         'Michael Pate': 2,\n",
       "         'Jon Roberts': 1,\n",
       "         'Jorge Ayala': 1,\n",
       "         'Mickey Munday': 1,\n",
       "         'Toni Mooney': 1,\n",
       "         'Nelson Andreu': 1,\n",
       "         'Al Sunshine': 1,\n",
       "         'Paul Newman': 23,\n",
       "         'George Kennedy': 24,\n",
       "         'Luke Askew': 6,\n",
       "         'Morgan Woodward': 2,\n",
       "         'Harry Dean Stanton': 37,\n",
       "         'Dennis Hopper': 37,\n",
       "         'Lou Antonio': 2,\n",
       "         'Robert Drivas': 1,\n",
       "         'Strother Martin': 12,\n",
       "         'Jo Van Fleet': 3,\n",
       "         'Clifton James': 9,\n",
       "         'Marc Cavell': 2,\n",
       "         'Richard Davalos': 4,\n",
       "         'Robert Donner': 7,\n",
       "         'J. D. Cannon': 2,\n",
       "         'Joe Don Baker': 17,\n",
       "         'James Gammon': 12,\n",
       "         'Chuck Hicks': 3,\n",
       "         'James Jeter': 3,\n",
       "         'Joy Harmon': 2,\n",
       "         'Anthony Zerbe': 11,\n",
       "         'Warren Finnerty': 1,\n",
       "         'John McLiam': 3,\n",
       "         'Wayne Rogers': 1,\n",
       "         'Charles Tyner': 4,\n",
       "         'Ralph Waite': 5,\n",
       "         'Buck Kartalian': 6,\n",
       "         'Kim Kahana': 2,\n",
       "         'Donn Pearce': 1,\n",
       "         'John Pearce': 2,\n",
       "         'Rush Williams': 1,\n",
       "         'Madonna': 15,\n",
       "         'Rupert Everett': 14,\n",
       "         'Benjamin Bratt': 19,\n",
       "         'Malcolm Stumpf': 1,\n",
       "         'Josef Sommer': 17,\n",
       "         'Suzanne Krull': 6,\n",
       "         'Linda Larkin': 2,\n",
       "         'Illeana Douglas': 14,\n",
       "         'Michael Vartan': 7,\n",
       "         'Lynn Redgrave': 8,\n",
       "         'Neil Patrick Harris': 18,\n",
       "         'Diane Keaton': 27,\n",
       "         'Carrie Preston': 11,\n",
       "         'Cynthia Nixon': 15,\n",
       "         'Alysia Reiner': 4,\n",
       "         'Josh Pais': 14,\n",
       "         'Claire van der Boom': 1,\n",
       "         'Sterling Jerins': 8,\n",
       "         'Liza J. Bennett': 3,\n",
       "         'Gary Wilmes': 4,\n",
       "         'Joanna Adler': 3,\n",
       "         'Jimmy Palumbo': 7,\n",
       "         'Hani Furstenberg': 1,\n",
       "         'Eric Sheffer Stevens': 2,\n",
       "         'Hannah Dunne': 2,\n",
       "         'Grace Rex': 2,\n",
       "         'Maddie Corman': 11,\n",
       "         'Miriam Shor': 3,\n",
       "         'Jackie Hoffman': 7,\n",
       "         'Jordan Baker': 3,\n",
       "         'Violet Krumbein': 1,\n",
       "         'Nadia Gan': 2,\n",
       "         'Jack Dimich': 1,\n",
       "         'Ilana Levine': 2,\n",
       "         'Katrina E. Perkins': 2,\n",
       "         'Anjili Pal': 1,\n",
       "         'Marcia DeBonis': 4,\n",
       "         'Henry Kelemen': 2,\n",
       "         'Diane Ciesla': 2,\n",
       "         'Jane Fergus': 1,\n",
       "         'Ted Sod': 1,\n",
       "         'Gregg Micheals': 1,\n",
       "         'Debra Messing': 9,\n",
       "         'Dermot Mulroney': 29,\n",
       "         'Amy Adams': 27,\n",
       "         'Jack Davenport': 9,\n",
       "         'Jeremy Sheffield': 4,\n",
       "         'Peter Egan': 4,\n",
       "         'Sarah Parish': 2,\n",
       "         'Holland Taylor': 14,\n",
       "         'Jay Simon': 1,\n",
       "         'Danielle Lewis': 1,\n",
       "         'Kseniya Rappoport': 1,\n",
       "         'Filippo Timi': 2,\n",
       "         'Antonia Truppo': 1,\n",
       "         'Gaetano Bruno': 1,\n",
       "         'Fausto Russo Alesi': 1,\n",
       "         'Michele Di Mauro': 1,\n",
       "         'Lorenzo Gioielli': 3,\n",
       "         'Lidia Vitale': 1,\n",
       "         'Giampiero Judica': 1,\n",
       "         'Roberto Accornero': 1,\n",
       "         'Lucia Poli': 1,\n",
       "         'Giorgio Colangeli': 2,\n",
       "         'Deborah Bernuzzi': 1,\n",
       "         'Barbara Braconi': 1,\n",
       "         'Federica Cassini': 1,\n",
       "         'Ray Romano': 11,\n",
       "         'John Leguizamo': 44,\n",
       "         'Denis Leary': 18,\n",
       "         'Queen Latifah': 29,\n",
       "         'Seann William Scott': 22,\n",
       "         'Josh Peck': 13,\n",
       "         'Jay Leno': 16,\n",
       "         'Will Arnett': 21,\n",
       "         'Chris Wedge': 5,\n",
       "         'Peter Ackerman': 2,\n",
       "         'Caitlin Rose Anderson': 2,\n",
       "         'Connor Anderson': 1,\n",
       "         'Joseph Bologna': 4,\n",
       "         'Jack Crocicchia': 1,\n",
       "         'Peter DeSeve': 1,\n",
       "         'Ariel Winter': 11,\n",
       "         'Clea Lewis': 6,\n",
       "         'Stephen Root': 38,\n",
       "         'Nicole DeFelice': 1,\n",
       "         'Debi Derryberry': 7,\n",
       "         'Marshall Efron': 5,\n",
       "         'Tom Fahn': 1,\n",
       "         'Jason Fricchione': 4,\n",
       "         'James Edmund Godwin': 1,\n",
       "         'George Jacobs': 2,\n",
       "         'Brian Scott McFadden': 2,\n",
       "         'Jansen Panettiere': 3,\n",
       "         'Gregory Romano': 1,\n",
       "         'Matthew Romano': 1,\n",
       "         'Carlos Saldanha': 2,\n",
       "         'Manoela Scarpa Saldanha': 1,\n",
       "         'Sofia Scarpa Saldanha': 1,\n",
       "         'Reyna Shaskan': 1,\n",
       "         'James Sie': 5,\n",
       "         'Cindy Slattery': 1,\n",
       "         'Mindy Sterling': 11,\n",
       "         'Alex Sullivan': 1,\n",
       "         'Ren√©e Taylor': 9,\n",
       "         'Alan Tudyk': 28,\n",
       "         'Claudia Besso': 2,\n",
       "         'Jess Harnell': 20,\n",
       "         'Madeleine Martin': 1,\n",
       "         'Kurt Russell': 30,\n",
       "         'Jennifer Jason Leigh': 18,\n",
       "         'Walton Goggins': 17,\n",
       "         'Demi√°n Bichir': 8,\n",
       "         'Tim Roth': 17,\n",
       "         'Michael Madsen': 21,\n",
       "         'Bruce Dern': 22,\n",
       "         'James Parks': 7,\n",
       "         'Dana Gourrier': 6,\n",
       "         'Zo√´ Bell': 9,\n",
       "         'Lee Horsley': 3,\n",
       "         'Gene Jones': 4,\n",
       "         'Keith Jefferson': 3,\n",
       "         'Craig Stark': 2,\n",
       "         'Belinda Owino': 2,\n",
       "         'Channing Tatum': 33,\n",
       "         'Quentin Tarantino': 14,\n",
       "         'Woody Harrelson': 44,\n",
       "         'Sandy Oian-Thomas': 1,\n",
       "         'Shaun Brown': 1,\n",
       "         'James Robert Miller': 1,\n",
       "         'Brett Gelman': 4,\n",
       "         'Mary Lynn Rajskub': 10,\n",
       "         'Laura Dern': 22,\n",
       "         'Judy Greer': 28,\n",
       "         'Cheryl Hines': 11,\n",
       "         'Isabella Amara': 3,\n",
       "         'James Saito': 8,\n",
       "         'Chris Carlson': 1,\n",
       "         'David Warshofsky': 16,\n",
       "         'Tom Proctor': 5,\n",
       "         'Matt Roy': 1,\n",
       "         'Bruce Bohne': 2,\n",
       "         'Roxy Wood': 1,\n",
       "         'Wade Thalberg': 1,\n",
       "         'Mason Sheehy': 1,\n",
       "         'Katie Rose Law': 1,\n",
       "         'Rachel Weber': 1,\n",
       "         'Toussaint Morrison': 1,\n",
       "         'Andrew Hawtrey': 1,\n",
       "         'Shawn J. Hamilton': 2,\n",
       "         'Tonita Castro': 4,\n",
       "         'Lauren Weedman': 5,\n",
       "         'Margo Martindale': 24,\n",
       "         'Mark Benninghoffen': 1,\n",
       "         'Richard Ooms': 3,\n",
       "         'Kimora Collins': 1,\n",
       "         'Joe Minjares': 3,\n",
       "         'Greta Oglesby': 1,\n",
       "         'Elizabeth Herron': 1,\n",
       "         'Miles Strommen': 1,\n",
       "         'Jackson Bond': 2,\n",
       "         'Bill McCallum': 1,\n",
       "         'Alec George': 1,\n",
       "         'Nate Mooney': 5,\n",
       "         'Paul Cram': 1,\n",
       "         'Adam Farabee': 1,\n",
       "         'Peter Moore': 2,\n",
       "         'Gene Larche': 1,\n",
       "         'Loren Lazerine': 2,\n",
       "         'Sally Wingert': 2,\n",
       "         'Patrick Stewart': 30,\n",
       "         'Jonathan Frakes': 4,\n",
       "         'Brent Spiner': 12,\n",
       "         'LeVar Burton': 6,\n",
       "         'Gates McFadden': 5,\n",
       "         'Marina Sirtis': 7,\n",
       "         'William Shatner': 17,\n",
       "         'James Doohan': 7,\n",
       "         'Walter Koenig': 7,\n",
       "         'Malcolm McDowell': 25,\n",
       "         'Alan Ruck': 9,\n",
       "         'Whoopi Goldberg': 32,\n",
       "         'Thomas Dekker': 4,\n",
       "         'Cameron Oppenheimer': 3,\n",
       "         ...})"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计出演电影最多的30位\n",
    "cast_counter = collections.Counter()\n",
    "for item in data['cast_info']:\n",
    "    for cast_ in item:\n",
    "        cast_counter[cast_[0]]+=1\n",
    "cast_counter\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Samuel L. Jackson', 80),\n",
       " ('Robert De Niro', 71),\n",
       " ('Bruce Willis', 62),\n",
       " ('Morgan Freeman', 61),\n",
       " ('Liam Neeson', 57),\n",
       " ('Willem Dafoe', 55),\n",
       " ('Steve Buscemi', 55),\n",
       " ('Sylvester Stallone', 54),\n",
       " ('Nicolas Cage', 54),\n",
       " ('Matt Damon', 51),\n",
       " ('J.K. Simmons', 50),\n",
       " ('John Goodman', 50),\n",
       " ('Julianne Moore', 50),\n",
       " ('Christopher Walken', 50),\n",
       " ('Robin Williams', 50),\n",
       " ('Johnny Depp', 48),\n",
       " ('Stanley Tucci', 47),\n",
       " ('Harrison Ford', 46),\n",
       " ('Richard Jenkins', 46),\n",
       " ('Ben Stiller', 46),\n",
       " ('Susan Sarandon', 46),\n",
       " ('Brad Pitt', 46),\n",
       " ('Tom Hanks', 45),\n",
       " ('Keith David', 44),\n",
       " ('John Leguizamo', 44),\n",
       " ('Woody Harrelson', 44),\n",
       " ('Bill Murray', 44),\n",
       " ('Dennis Quaid', 44),\n",
       " ('James Franco', 44),\n",
       " ('Dustin Hoffman', 43)]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast30 = sorted(list(cast_counter.items()), key=lambda x:x[1],reverse=True)\n",
    "cast30[:30] # 出演电影多的果然以年纪较大的男演员为主"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Samuel L. Jackson', 80),\n",
       " ('Robert De Niro', 71),\n",
       " ('Bruce Willis', 62),\n",
       " ('Morgan Freeman', 61),\n",
       " ('Liam Neeson', 57),\n",
       " ('Willem Dafoe', 55),\n",
       " ('Steve Buscemi', 55),\n",
       " ('Sylvester Stallone', 54),\n",
       " ('Nicolas Cage', 54),\n",
       " ('Matt Damon', 51),\n",
       " ('J.K. Simmons', 50),\n",
       " ('John Goodman', 50),\n",
       " ('Julianne Moore', 50),\n",
       " ('Christopher Walken', 50),\n",
       " ('Robin Williams', 50),\n",
       " ('Johnny Depp', 48),\n",
       " ('Stanley Tucci', 47),\n",
       " ('Harrison Ford', 46),\n",
       " ('Richard Jenkins', 46),\n",
       " ('Ben Stiller', 46),\n",
       " ('Susan Sarandon', 46),\n",
       " ('Brad Pitt', 46),\n",
       " ('Tom Hanks', 45),\n",
       " ('Keith David', 44),\n",
       " ('John Leguizamo', 44),\n",
       " ('Woody Harrelson', 44),\n",
       " ('Bill Murray', 44),\n",
       " ('Dennis Quaid', 44),\n",
       " ('James Franco', 44),\n",
       " ('Dustin Hoffman', 43),\n",
       " ('Owen Wilson', 43),\n",
       " ('Paul Giamatti', 43),\n",
       " ('John Cusack', 42),\n",
       " ('Ben Affleck', 42),\n",
       " ('Gene Hackman', 42),\n",
       " ('John Turturro', 42),\n",
       " ('Ben Kingsley', 42),\n",
       " ('Alec Baldwin', 42),\n",
       " ('Tom Cruise', 41),\n",
       " ('Forest Whitaker', 41),\n",
       " ('Robert Duvall', 41),\n",
       " ('Frank Welker', 40),\n",
       " ('Michael Caine', 40),\n",
       " ('Denzel Washington', 40),\n",
       " ('John Hurt', 40),\n",
       " ('Robert Downey Jr.', 40),\n",
       " ('Kevin Costner', 40),\n",
       " ('Donald Sutherland', 39),\n",
       " ('William H. Macy', 39),\n",
       " ('Dan Aykroyd', 39)]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast30[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 演电影多的前50位演员最少演了39部电影，前150名，最少演了31部\n",
    "# 为限制数据集规模，选择前30位做one-hot,前30位最少演了43部"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "cast_30 = cast30[:30]\n",
    "for cast_ in cast_30:\n",
    "    data[cast_[0]] = data['cast_info'].map(lambda x: 1 if cast_[0] in x else 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 统计男女性演员数量\n",
    "# 性别竟然有3种，0，1，2,\n",
    "# 也不知道是未知？还是美国人逆天\n",
    "data['gender_0'] = data['cast_info'].map(lambda x: sum([i[1]==0 for i in x]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id\n",
       "1        6\n",
       "2        0\n",
       "3       31\n",
       "4        4\n",
       "5        0\n",
       "        ..\n",
       "7394     0\n",
       "7395     8\n",
       "7396     0\n",
       "7397    12\n",
       "7398    16\n",
       "Name: gender_0, Length: 7398, dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['gender_0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['Rob Corddry', 2], ['Craig Robinson', 2], ['Clark Duke', 2], ['Adam Scott', 2], ['Chevy Chase', 2], ['Gillian Jacobs', 1], ['Bianca Haase', 1], ['Collette Wolfe', 1], ['Kumail Nanjiani', 2], ['Kellee Stewart', 1], ['Josh Heald', 2], ['Gretchen Koerner', 0], ['Lisa Loeb', 1], ['Jessica Williams', 1], ['Bruce Buffer', 0], ['Mariana Paola Vicente', 0], ['Christian Slater', 2], ['Jason Jones', 0], ['Olivia Jordan', 0], ['Christine Bently', 1], ['Stacey Asaro', 0], ['John Cusack', 2], ['Adam Herschman', 2], ['Kisha Sierra', 1]]\n"
     ]
    }
   ],
   "source": [
    "for x in data['cast_info'][:1]:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['gender_1'] = data['cast_info'].map(lambda x: sum([i[1]==1 for i in x]))\n",
    "data['gender_2'] = data['cast_info'].map(lambda x: sum([i[1]==2 for i in x]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keywords                 669\n",
    "# production_companies     414\n",
    "# production_countries     157\n",
    "# crew                      38"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'credit_id': '59ac067c92514107af02c8c8', 'department': 'Directing', 'gender': 0, 'id': 1449071, 'job': 'First Assistant Director', 'name': 'Kelly Cantley', 'profile_path': None}, {'credit_id': '52fe4ee7c3a36847f82afad7', 'department': 'Directing', 'gender': 2, 'id': 3227, 'job': 'Director', 'name': 'Steve Pink', 'profile_path': '/myHOgo8mQSCiCAZNGMRdHVr03jr.jpg'}, {'credit_id': '5524ed25c3a3687ded000d88', 'department': 'Writing', 'gender': 2, 'id': 347335, 'job': 'Writer', 'name': 'Josh Heald', 'profile_path': '/pwXJIenrDMrG7t3zNfLvr8w1RGU.jpg'}, {'credit_id': '5524ed2d925141720c001128', 'department': 'Writing', 'gender': 2, 'id': 347335, 'job': 'Characters', 'name': 'Josh Heald', 'profile_path': '/pwXJIenrDMrG7t3zNfLvr8w1RGU.jpg'}, {'credit_id': '5524ed3d92514166c1004a5d', 'department': 'Production', 'gender': 2, 'id': 57822, 'job': 'Producer', 'name': 'Andrew Panay', 'profile_path': None}, {'credit_id': '5524ed4bc3a3687df3000dd2', 'department': 'Production', 'gender': 0, 'id': 1451395, 'job': 'Associate Producer', 'name': 'Adam Blum', 'profile_path': None}, {'credit_id': '5524ed5a925141720c00112c', 'department': 'Production', 'gender': 2, 'id': 52997, 'job': 'Executive Producer', 'name': 'Rob Corddry', 'profile_path': '/k2zJL0V1nEZuFT08xUdOd3ucfXz.jpg'}, {'credit_id': '5524ed85c3a3687e0e000f56', 'department': 'Production', 'gender': 0, 'id': 62807, 'job': 'Executive Producer', 'name': 'Ben Ormand', 'profile_path': None}, {'credit_id': '5524ed9fc3a3687e0e000f59', 'department': 'Sound', 'gender': 2, 'id': 23486, 'job': 'Original Music Composer', 'name': 'Christophe Beck', 'profile_path': '/2fnJUmCk6IEpVIptpYaUk31epHx.jpg'}, {'credit_id': '5524eda6c3a3687e03000d28', 'department': 'Camera', 'gender': 2, 'id': 6117, 'job': 'Director of Photography', 'name': 'Declan Quinn', 'profile_path': None}, {'credit_id': '5524edb4925141720c00113d', 'department': 'Editing', 'gender': 0, 'id': 1451396, 'job': 'Editor', 'name': 'Jamie Gross', 'profile_path': None}, {'credit_id': '5524edc1925141727600102e', 'department': 'Production', 'gender': 0, 'id': 22219, 'job': 'Casting', 'name': 'Susie Farris', 'profile_path': None}, {'credit_id': '5524edd192514171cb008257', 'department': 'Art', 'gender': 0, 'id': 1002643, 'job': 'Production Design', 'name': 'Ryan Berg', 'profile_path': None}, {'credit_id': '555ad9be9251411e5b00d485', 'department': 'Production', 'gender': 2, 'id': 57431, 'job': 'Executive Producer', 'name': 'Matt Moore', 'profile_path': None}, {'credit_id': '5677e93bc3a36816890087dc', 'department': 'Directing', 'gender': 0, 'id': 1551818, 'job': 'Script Supervisor', 'name': 'Nicole Garcea', 'profile_path': None}, {'credit_id': '5677e96a92514179e10093d0', 'department': 'Production', 'gender': 0, 'id': 1551819, 'job': 'Production Coordinator', 'name': 'Jason Salzman', 'profile_path': None}, {'credit_id': '5677e98492514179d2008cd9', 'department': 'Costume & Make-Up', 'gender': 0, 'id': 1422996, 'job': 'Costume Design', 'name': 'Carol Cutshall', 'profile_path': None}, {'credit_id': '5677e9d5c3a368168e009414', 'department': 'Art', 'gender': 2, 'id': 500199, 'job': 'Set Decoration', 'name': 'Tim Cohn', 'profile_path': None}, {'credit_id': '5677f89d9251417845001a61', 'department': 'Costume & Make-Up', 'gender': 0, 'id': 1527917, 'job': 'Hair Department Head', 'name': 'Voni Hinkle', 'profile_path': None}, {'credit_id': '5677f8b392514179dd0089fb', 'department': 'Costume & Make-Up', 'gender': 0, 'id': 1431554, 'job': 'Makeup Department Head', 'name': 'Remi Savva', 'profile_path': None}, {'credit_id': '5677f8d1c3a3681689008a4b', 'department': 'Art', 'gender': 0, 'id': 66495, 'job': 'Art Direction', 'name': 'Jason Baldwin Stewart', 'profile_path': None}, {'credit_id': '5677f8eec3a3681685008dd5', 'department': 'Production', 'gender': 0, 'id': 1412466, 'job': 'Production Supervisor', 'name': 'Korey Budd', 'profile_path': None}, {'credit_id': '5677f90a9251417845001a7d', 'department': 'Sound', 'gender': 0, 'id': 1401562, 'job': 'Sound Re-Recording Mixer', 'name': 'Gary C. Bourgeois', 'profile_path': None}, {'credit_id': '5677f91e9251417845001a84', 'department': 'Sound', 'gender': 0, 'id': 1396794, 'job': 'Sound Re-Recording Mixer', 'name': 'Gabriel J. Serrano', 'profile_path': None}, {'credit_id': '5677f938c3a3681680008dd4', 'department': 'Editing', 'gender': 0, 'id': 13168, 'job': 'Dialogue Editor', 'name': 'Victoria Rose Sampson', 'profile_path': None}, {'credit_id': '5677f94e92514179dd008a1f', 'department': 'Sound', 'gender': 0, 'id': 1551839, 'job': 'Production Sound Mixer', 'name': 'Michael B. Koff', 'profile_path': None}, {'credit_id': '5677f968c3a368168e009698', 'department': 'Sound', 'gender': 0, 'id': 113052, 'job': 'Sound Effects Editor', 'name': 'Randall Guth', 'profile_path': None}, {'credit_id': '5677f98dc3a3681685008e02', 'department': 'Crew', 'gender': 2, 'id': 1442535, 'job': 'Stunt Coordinator', 'name': 'Chuck Picerni Jr.', 'profile_path': '/yE5QtXUzcrnCzMRctZL8F5g842B.jpg'}, {'credit_id': '5677f9a692514179dd008a49', 'department': 'Camera', 'gender': 0, 'id': 1437305, 'job': 'Camera Operator', 'name': 'Michael Applebaum', 'profile_path': None}, {'credit_id': '5677f9bd9251417845001aae', 'department': 'Camera', 'gender': 0, 'id': 1401765, 'job': 'Still Photographer', 'name': 'Steve Dietl', 'profile_path': None}, {'credit_id': '5677f9e592514179e7008bf7', 'department': 'Lighting', 'gender': 0, 'id': 1402721, 'job': 'Rigging Gaffer', 'name': 'Tarik Naim Alherimi', 'profile_path': None}, {'credit_id': '5677f9f4c3a368167c0090ed', 'department': 'Lighting', 'gender': 0, 'id': 1402719, 'job': 'Gaffer', 'name': 'Paul Olinde', 'profile_path': None}, {'credit_id': '5677fa21c3a368168e0096ca', 'department': 'Sound', 'gender': 0, 'id': 1551840, 'job': 'Music Supervisor', 'name': 'Steve Griffen', 'profile_path': None}, {'credit_id': '5677fa31c3a3681680008e04', 'department': 'Sound', 'gender': 0, 'id': 1551841, 'job': 'Music Editor', 'name': 'Matt Fausak', 'profile_path': None}, {'credit_id': '5677fa4392514179dd008a76', 'department': 'Sound', 'gender': 0, 'id': 1551840, 'job': 'Music Editor', 'name': 'Steve Griffen', 'profile_path': None}, {'credit_id': '5677fa609251417845001acf', 'department': 'Costume & Make-Up', 'gender': 0, 'id': 1403416, 'job': 'Costume Supervisor', 'name': 'Shonta T. McCray', 'profile_path': None}, {'credit_id': '5677fa8492514179d2008fb3', 'department': 'Camera', 'gender': 0, 'id': 1425831, 'job': 'Steadicam Operator', 'name': 'Mark Karavite', 'profile_path': None}, {'credit_id': '5677fab2c3a3681689008ac3', 'department': 'Camera', 'gender': 0, 'id': 1551842, 'job': 'First Assistant Camera', 'name': 'Joe Waistell', 'profile_path': None}, {'credit_id': '5677faecc3a368168e0096fe', 'department': 'Sound', 'gender': 0, 'id': 58362, 'job': 'Supervising Sound Editor', 'name': 'Michael Hilkene', 'profile_path': None}, {'credit_id': '59ac0368c3a3682c0a02c484', 'department': 'Crew', 'gender': 0, 'id': 1881584, 'job': 'Additional Writing', 'name': 'John Karnay', 'profile_path': None}, {'credit_id': '59ac0411c3a3682bf0028966', 'department': 'Costume & Make-Up', 'gender': 0, 'id': 1431552, 'job': 'Hairstylist', 'name': 'Daina Daigle', 'profile_path': None}, {'credit_id': '59ac0504925141072302b8fb', 'department': 'Costume & Make-Up', 'gender': 0, 'id': 1712001, 'job': 'Makeup Artist', 'name': 'Allison Gordin', 'profile_path': None}, {'credit_id': '59ac0570c3a3682bf0028aac', 'department': 'Costume & Make-Up', 'gender': 0, 'id': 578725, 'job': 'Makeup Artist', 'name': 'Darryl Lucas', 'profile_path': None}, {'credit_id': '59ac05a4925141077e02c97e', 'department': 'Costume & Make-Up', 'gender': 0, 'id': 1463274, 'job': 'Makeup Artist', 'name': 'Annabelle MacNeal', 'profile_path': None}, {'credit_id': '59ac05c6925141076502d106', 'department': 'Costume & Make-Up', 'gender': 0, 'id': 1881586, 'job': 'Makeup Artist', 'name': 'Marina Savva', 'profile_path': None}, {'credit_id': '59ac0615c3a3682c480296aa', 'department': 'Costume & Make-Up', 'gender': 0, 'id': 1406267, 'job': 'Hairstylist', 'name': 'Carl G. Variste', 'profile_path': None}, {'credit_id': '59ac06ba925141076502d1fa', 'department': 'Directing', 'gender': 0, 'id': 1798593, 'job': 'First Assistant Director', 'name': 'Josh King', 'profile_path': None}, {'credit_id': '59ac06f1c3a3682c2202aca0', 'department': 'Art', 'gender': 0, 'id': 1415083, 'job': 'Greensman', 'name': 'Scott C. Bivona', 'profile_path': None}, {'credit_id': '59ac072c925141076502d260', 'department': 'Art', 'gender': 0, 'id': 1881587, 'job': 'Title Designer', 'name': 'Eunha Choi', 'profile_path': None}, {'credit_id': '59ac077c925141077e02cb62', 'department': 'Art', 'gender': 0, 'id': 1585302, 'job': 'Construction Coordinator', 'name': 'Daniel Coe', 'profile_path': None}, {'credit_id': '59ac07e0925141078a02d842', 'department': 'Art', 'gender': 0, 'id': 1495523, 'job': 'Set Designer', 'name': 'Spencer Davison', 'profile_path': None}, {'credit_id': '59ac0862925141072f02cf6f', 'department': 'Art', 'gender': 0, 'id': 1881589, 'job': 'Painter', 'name': 'Sonia L. Garcia', 'profile_path': None}, {'credit_id': '59ac08e0c3a3682bf0028e51', 'department': 'Art', 'gender': 0, 'id': 1424896, 'job': 'Art Department Coordinator', 'name': 'Caleb Guillotte', 'profile_path': None}, {'credit_id': '59ac0920c3a3682c2202af36', 'department': 'Art', 'gender': 0, 'id': 1393375, 'job': 'Leadman', 'name': \"Pat A. O'Connor\", 'profile_path': None}, {'credit_id': '59ac095592514107af02cc39', 'department': 'Art', 'gender': 0, 'id': 1881592, 'job': 'Set Designer', 'name': 'Brendan Turrill', 'profile_path': None}, {'credit_id': '59ac0989925141072302bdfa', 'department': 'Art', 'gender': 2, 'id': 76497, 'job': 'Property Master', 'name': 'Brook Yeaton', 'profile_path': None}, {'credit_id': '59ac0a2cc3a3682c9c02add1', 'department': 'Sound', 'gender': 0, 'id': 1881596, 'job': 'Boom Operator', 'name': 'Matthew Armstrong', 'profile_path': None}, {'credit_id': '59ac0aa8925141072f02d282', 'department': 'Visual Effects', 'gender': 2, 'id': 1558086, 'job': 'Special Effects Supervisor', 'name': 'Matt Kutcher', 'profile_path': None}, {'credit_id': '59ac0b2ac3a3682c2202b192', 'department': 'Crew', 'gender': 2, 'id': 1558087, 'job': 'Special Effects Coordinator', 'name': 'Eric Roberts', 'profile_path': None}, {'credit_id': '59ac0b7ac3a3682c2202b1fb', 'department': 'Visual Effects', 'gender': 0, 'id': 1392098, 'job': 'Visual Effects Supervisor', 'name': 'Rocco Passionino', 'profile_path': None}, {'credit_id': '59ac0bbe925141077e02d0c4', 'department': 'Visual Effects', 'gender': 0, 'id': 1558716, 'job': 'Visual Effects Coordinator', 'name': 'Joseph Payo', 'profile_path': None}, {'credit_id': '59ac0bf2c3a3682cc802cefa', 'department': 'Visual Effects', 'gender': 0, 'id': 1408784, 'job': 'Visual Effects Producer', 'name': 'Chris Roff', 'profile_path': None}, {'credit_id': '59ac0c51c3a3682c48029d99', 'department': 'Lighting', 'gender': 0, 'id': 1881600, 'job': 'Best Boy Electric', 'name': 'Ulyan Atamanyuk', 'profile_path': None}, {'credit_id': '59ac0cbac3a3682c0a02cff6', 'department': 'Camera', 'gender': 0, 'id': 1881602, 'job': 'Key Grip', 'name': 'Chris Ekstrom', 'profile_path': None}, {'credit_id': '59ac0d54925141072f02d5e6', 'department': 'Lighting', 'gender': 0, 'id': 1484984, 'job': 'Best Boy Electric', 'name': 'Brad Garris', 'profile_path': None}, {'credit_id': '59ac0db0925141078a02df86', 'department': 'Camera', 'gender': 0, 'id': 1881603, 'job': 'Dolly Grip', 'name': 'Kendell Joseph', 'profile_path': None}, {'credit_id': '59ac0e5a925141077e02d39f', 'department': 'Camera', 'gender': 0, 'id': 1549179, 'job': 'Dolly Grip', 'name': 'Spencer Wilcox', 'profile_path': None}, {'credit_id': '59ac0e9f925141079d02bee6', 'department': 'Costume & Make-Up', 'gender': 0, 'id': 1552626, 'job': 'Key Costumer', 'name': 'Sarah P. Koeppe', 'profile_path': None}, {'credit_id': '59ac0ec1c3a3682bf0029524', 'department': 'Costume & Make-Up', 'gender': 0, 'id': 1881605, 'job': 'Seamstress', 'name': 'Catherine Rodi', 'profile_path': None}, {'credit_id': '59ac0eef925141070702c7ff', 'department': 'Costume & Make-Up', 'gender': 0, 'id': 1463801, 'job': 'Seamstress', 'name': 'Giselle Spence', 'profile_path': None}, {'credit_id': '59ac0f5dc3a3682c4802a0f5', 'department': 'Production', 'gender': 0, 'id': 1400837, 'job': 'Location Manager', 'name': 'John A. Johnston', 'profile_path': None}, {'credit_id': '59ac0ff2c3a3682c4802a196', 'department': 'Crew', 'gender': 0, 'id': 1844322, 'job': 'Production Controller', 'name': 'Gail Marks', 'profile_path': None}]\n",
      "\n",
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Walker', 'profile_path': None}, {'credit_id': '5540b0b5c3a3681da50016cc', 'department': 'Crew', 'gender': 2, 'id': 1372414, 'job': 'Special Effects Coordinator', 'name': 'Jak Osmond', 'profile_path': None}, {'credit_id': '5540b6519251411fcb00003d', 'department': 'Crew', 'gender': 0, 'id': 1378734, 'job': 'Studio Teachers', 'name': 'Natalie Zara Smith', 'profile_path': None}, {'credit_id': '5540aae39251413d6d00117f', 'department': 'Costume & Make-Up', 'gender': 1, 'id': 1384361, 'job': 'Makeup Department Head', 'name': 'Norma Hill-Patton', 'profile_path': None}, {'credit_id': '5540b2d2c3a3681d9c001753', 'department': 'Camera', 'gender': 0, 'id': 1394972, 'job': 'Steadicam Operator', 'name': 'Norbert Kaluza', 'profile_path': None}, {'credit_id': '5540b223c3a3681da00015e6', 'department': 'Camera', 'gender': 0, 'id': 1395322, 'job': 'Camera Operator', 'name': 'Andrew Fisher', 'profile_path': None}, {'credit_id': '5540b30e9251414af0001679', 'department': 'Lighting', 'gender': 0, 'id': 1398846, 'job': 'Gaffer', 'name': 'John Dekker', 'profile_path': None}, {'credit_id': '5540b381c3a3682a740010c3', 'department': 'Camera', 'gender': 0, 'id': 1399071, 'job': 'Helicopter Camera', 'name': 'Hans Bjerno', 'profile_path': '/tzeE3ATzHWyEQH32GYeGuSgr1xz.jpg'}, {'credit_id': '5540b32b9251414ae600168a', 'department': 'Camera', 'gender': 0, 'id': 1400408, 'job': 'Still Photographer', 'name': 'Diyah Pera', 'profile_path': None}, {'credit_id': '5540b4859251414ae60016ad', 'department': 'Editing', 'gender': 0, 'id': 1400414, 'job': 'Digital Intermediate', 'name': 'Jay Harada', 'profile_path': None}, {'credit_id': '5540b53e9251411fcb000028', 'department': 'Crew', 'gender': 0, 'id': 1400417, 'job': 'Transportation Coordinator', 'name': 'Brian Whitlock', 'profile_path': None}, {'credit_id': '5540ac8e9251414ae60015f4', 'department': 'Crew', 'gender': 0, 'id': 1410328, 'job': 'Makeup Effects', 'name': 'C√©line Godeau', 'profile_path': None}, {'credit_id': '5540b24cc3a3681da50016e5', 'department': 'Camera', 'gender': 0, 'id': 1410582, 'job': 'Camera Operator', 'name': 'Scott MacDonald', 'profile_path': None}, {'credit_id': '5540ab3ec3a36829e5001228', 'department': 'Crew', 'gender': 0, 'id': 1418353, 'job': 'Makeup Effects', 'name': 'Vicki Syskakis', 'profile_path': None}, {'credit_id': '5540b5099251413d6d001268', 'department': 'Sound', 'gender': 0, 'id': 1419924, 'job': 'Music Editor', 'name': 'Maarten Hofmeijer', 'profile_path': None}, {'credit_id': '5540affc9251414af0001634', 'department': 'Sound', 'gender': 0, 'id': 1425911, 'job': 'Dolby Consultant', 'name': 'Bryan Pennington', 'profile_path': None}, {'credit_id': '5540ad1d9251414ae6001602', 'department': 'Crew', 'gender': 0, 'id': 1431014, 'job': 'Property Master', 'name': 'Dean Barker', 'profile_path': None}, {'credit_id': '5540b2789251413d6d00122c', 'department': 'Lighting', 'gender': 0, 'id': 1438623, 'job': 'Gaffer', 'name': 'Paul Slatter', 'profile_path': None}, {'credit_id': '5540b603c3a3681d9c001796', 'department': 'Production', 'gender': 0, 'id': 1438630, 'job': 'Location Manager', 'name': 'Terry Mackay', 'profile_path': None}, {'credit_id': '5540ab1bc3a3681da5001636', 'department': 'Costume & Make-Up', 'gender': 0, 'id': 1441345, 'job': 'Makeup Artist', 'name': 'Tanya Hudson', 'profile_path': None}, {'credit_id': '5540b200c3a36829e50012d0', 'department': 'Camera', 'gender': 0, 'id': 1441368, 'job': 'Camera Operator', 'name': 'Glen A. Dickson', 'profile_path': None}, {'credit_id': '554ae23cc3a3685e50000bf3', 'department': 'Visual Effects', 'gender': 0, 'id': 1460599, 'job': 'VFX Artist', 'name': 'Andy Asperin', 'profile_path': None}, {'credit_id': '5540aaff9251414af0001596', 'department': 'Costume & Make-Up', 'gender': 0, 'id': 1460753, 'job': 'Makeup Artist', 'name': 'Lisa Strong', 'profile_path': None}, {'credit_id': '5540ab68c3a3682a7400100f', 'department': 'Costume & Make-Up', 'gender': 0, 'id': 1460754, 'job': 'Hairstylist', 'name': 'Jennifer Amberson', 'profile_path': None}, {'credit_id': '5540abe6c3a3681dab001626', 'department': 'Costume & Make-Up', 'gender': 2, 'id': 1460755, 'job': 'Hairstylist', 'name': 'Paul Edwards', 'profile_path': None}, {'credit_id': '5540ac719251414af90014fd', 'department': 'Costume & Make-Up', 'gender': 0, 'id': 1460756, 'job': 'Hairstylist', 'name': 'Carol Raskin', 'profile_path': None}, {'credit_id': '5540ad6bc3a36877ee000ebd', 'department': 'Art', 'gender': 0, 'id': 1460758, 'job': 'Production Design', 'name': \"Rachel O'Toole\", 'profile_path': None}, {'credit_id': '5540af389251414af000161e', 'department': 'Art', 'gender': 0, 'id': 1460760, 'job': 'Assistant Art Director', 'name': 'Randy Hutniak', 'profile_path': None}, {'credit_id': '5540af66c3a3681d98001654', 'department': 'Crew', 'gender': 0, 'id': 1460761, 'job': 'Property Master', 'name': 'David Rosychuk', 'profile_path': None}, {'credit_id': '5540b11ac3a3682a74001099', 'department': 'Crew', 'gender': 0, 'id': 1460762, 'job': 'CG Supervisor', 'name': 'Richard Patterson', 'profile_path': None}, {'credit_id': '5540b18dc3a3681d9c00172c', 'department': 'Visual Effects', 'gender': 0, 'id': 1460763, 'job': 'Visual Effects Supervisor', 'name': 'Adam Stern', 'profile_path': None}, {'credit_id': '5540b2949251414aee001626', 'department': 'Lighting', 'gender': 0, 'id': 1460765, 'job': 'Gaffer', 'name': 'Rusty Pouch', 'profile_path': None}, {'credit_id': '5540b2b79251414ae8001693', 'department': 'Lighting', 'gender': 0, 'id': 1460766, 'job': 'Rigging Gaffer', 'name': \"Jeff O'Brian\", 'profile_path': None}, {'credit_id': '5540b2eec3a3681d980016a6', 'department': 'Camera', 'gender': 0, 'id': 1460767, 'job': 'Steadicam Operator', 'name': 'James Baldanza', 'profile_path': None}, {'credit_id': '5540b3419251413d6d00123e', 'department': 'Camera', 'gender': 0, 'id': 1460768, 'job': 'Still Photographer', 'name': 'Glenn Watson', 'profile_path': None}, {'credit_id': '5540b3bd9251414af3001699', 'department': 'Costume & Make-Up', 'gender': 0, 'id': 1460769, 'job': 'Costume Supervisor', 'name': 'Jacqui Gee', 'profile_path': None}, {'credit_id': '5540b3e0c3a3681d980016be', 'department': 'Costume & Make-Up', 'gender': 0, 'id': 1460772, 'job': 'Set Costumer', 'name': 'Charron Hume', 'profile_path': None}, {'credit_id': '5540b41ac3a3681d980016c5', 'department': 'Costume & Make-Up', 'gender': 0, 'id': 1460775, 'job': 'Set Costumer', 'name': 'James Spencer', 'profile_path': None}, {'credit_id': '5540b4589251414ae60016a4', 'department': 'Editing', 'gender': 0, 'id': 1460776, 'job': 'Digital Intermediate', 'name': 'James Cowan', 'profile_path': None}, {'credit_id': '5540b472c3a3681da500170b', 'department': 'Editing', 'gender': 0, 'id': 1460777, 'job': 'Digital Intermediate', 'name': 'Christine Dougherty', 'profile_path': None}, {'credit_id': '5540b49c9251413d6d00125d', 'department': 'Editing', 'gender': 0, 'id': 1460778, 'job': 'Digital Intermediate', 'name': 'Catherine McQuaid', 'profile_path': None}, {'credit_id': '5540b4c59251414ae80016b5', 'department': 'Editing', 'gender': 0, 'id': 1460781, 'job': 'Digital Intermediate', 'name': 'Christine Vasquez', 'profile_path': None}, {'credit_id': '5540b4e29251414ae60016bd', 'department': 'Editing', 'gender': 0, 'id': 1460782, 'job': 'First Assistant Editor', 'name': 'Bryan Lamoureux', 'profile_path': None}, {'credit_id': '5540b56ec3a3681d9c001789', 'department': 'Crew', 'gender': 0, 'id': 1460784, 'job': 'Choreographer', 'name': 'Kimberly Sato', 'profile_path': None}, {'credit_id': '5540b5879251414ae80016cb', 'department': 'Directing', 'gender': 1, 'id': 1460785, 'job': 'Script Supervisor', 'name': 'Ilene Pickus', 'profile_path': None}, {'credit_id': '5540b5a0c3a3681dab001705', 'department': 'Directing', 'gender': 0, 'id': 1460786, 'job': 'Script Supervisor', 'name': 'Stephanie Rossel', 'profile_path': None}, {'credit_id': '5540b5eac3a36829e5001323', 'department': 'Directing', 'gender': 0, 'id': 1460788, 'job': 'Script Supervisor', 'name': 'Elspeth Grafton', 'profile_path': None}, {'credit_id': '5540b627c3a36829e500132c', 'department': 'Production', 'gender': 1, 'id': 1460790, 'job': 'Location Manager', 'name': 'Jennifer Radzikowski', 'profile_path': None}, {'credit_id': '56c4e32b9251412696000865', 'department': 'Camera', 'gender': 0, 'id': 1579180, 'job': 'First Assistant Camera', 'name': 'Stewart Whelan', 'profile_path': None}]\n",
      "\n",
      "[{'credit_id': '52fe47a69251416c750a0daf', 'department': 'Directing', 'gender': 1, 'id': 115892, 'job': 'Director', 'name': 'Jehane Noujaim', 'profile_path': None}]\n",
      "\n",
      "[{'credit_id': '52fe43c89251416c7501deb3', 'department': 'Directing', 'gender': 2, 'id': 65298, 'job': 'Director', 'name': 'Brian Henson', 'profile_path': '/m2Bczi1gvhnIYCGp8Fhg2QCPuNf.jpg'}, {'credit_id': '52fe43c89251416c7501deb9', 'department': 'Production', 'gender': 2, 'id': 7908, 'job': 'Producer', 'name': 'Frank Oz', 'profile_path': '/aLH5bYwMIlVxCe4rIDaEsVJqDKn.jpg'}, {'credit_id': '52fe43c89251416c7501debf', 'department': 'Production', 'gender': 2, 'id': 65298, 'job': 'Producer', 'name': 'Brian Henson', 'profile_path': '/m2Bczi1gvhnIYCGp8Fhg2QCPuNf.jpg'}, {'credit_id': '52fe43c89251416c7501dec5', 'department': 'Writing', 'gender': 2, 'id': 64184, 'job': 'Screenplay', 'name': 'Jerry Juhl', 'profile_path': '/cgNumNNGSb5MeN0WIXkkhY0iXGV.jpg'}, {'credit_id': '52fe43c89251416c7501decb', 'department': 'Writing', 'gender': 2, 'id': 29533, 'job': 'Novel', 'name': 'Robert Louis Stevenson', 'profile_path': '/fGEGp5kpR2mhX89XqAJoJQFGeuG.jpg'}, {'credit_id': '52fe43c89251416c7501ded1', 'department': 'Sound', 'gender': 2, 'id': 947, 'job': 'Original Music Composer', 'name': 'Hans Zimmer', 'profile_path': '/7IjJpvGtCfY0DsritmfCh2iX9I4.jpg'}, {'credit_id': '52fe43c89251416c7501ded7', 'department': 'Editing', 'gender': 2, 'id': 12940, 'job': 'Editor', 'name': 'Michael Jablow', 'profile_path': None}, {'credit_id': '546892b422136e68d50007c5', 'department': 'Camera', 'gender': 0, 'id': 1385880, 'job': 'Director of Photography', 'name': 'John Fenner', 'profile_path': None}]\n",
      "\n",
      "[{'credit_id': '52fe45609251416c750545b3', 'department': 'Directing', 'gender': 2, 'id': 13524, 'job': 'Director', 'name': 'Christopher Guest', 'profile_path': '/fhEQq0q2aR2sh4HU824xunoyAce.jpg'}, {'credit_id': '52fe45609251416c750545b9', 'department': 'Writing', 'gender': 2, 'id': 13524, 'job': 'Writer', 'name': 'Christopher Guest', 'profile_path': '/fhEQq0q2aR2sh4HU824xunoyAce.jpg'}, {'credit_id': '541658c70e0a261c35003e18', 'department': 'Writing', 'gender': 2, 'id': 26510, 'job': 'Writer', 'name': 'Eugene Levy', 'profile_path': '/69IBiDjU1gSqtrcGOA7PA7aEYsc.jpg'}, {'credit_id': '541658d8c3a3684d0a003f84', 'department': 'Production', 'gender': 1, 'id': 6467, 'job': 'Producer', 'name': 'Karen Murphy', 'profile_path': None}, {'credit_id': '54165a000e0a261c2a003eef', 'department': 'Camera', 'gender': 1, 'id': 1064271, 'job': 'Director of Photography', 'name': 'Arlene Nelson', 'profile_path': None}, {'credit_id': '54165a0fc3a3684d0a003fbf', 'department': 'Editing', 'gender': 2, 'id': 3032, 'job': 'Editor', 'name': 'Robert Leighton', 'profile_path': None}, {'credit_id': '54165a1b0e0a261c38003c7b', 'department': 'Art', 'gender': 0, 'id': 81893, 'job': 'Production Design', 'name': 'Joseph T. Garrity', 'profile_path': None}, {'credit_id': '54165a28c3a3684d0a003fc4', 'department': 'Art', 'gender': 0, 'id': 35166, 'job': 'Art Direction', 'name': 'Pat Tagliaferro', 'profile_path': None}, {'credit_id': '548591a9c3a3680b36002eee', 'department': 'Costume & Make-Up', 'gender': 1, 'id': 958835, 'job': 'Costume Design', 'name': 'Durinda Wood', 'profile_path': None}, {'credit_id': '548591d29251416a6f002807', 'department': 'Production', 'gender': 0, 'id': 1396364, 'job': 'Line Producer', 'name': 'Donna E. Bloom', 'profile_path': None}, {'credit_id': '548591e49251416a5d002d9c', 'department': 'Art', 'gender': 1, 'id': 960963, 'job': 'Set Decoration', 'name': 'Dena Roth', 'profile_path': None}]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# crew：职员（导演/编辑/摄影...）的姓名/id/性别，使用json格式\n",
    "print_feat(data, 'crew')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 增加一列crew_nums表示职员人数，一般来说越是大片，人数越多\n",
    "# 首先提取crew信息\n",
    "def crew_abstract(x):\n",
    "    \"\"\"\n",
    "    提取各部门人数，性别\n",
    "    \"\"\"\n",
    "    dic = collections.defaultdict(int)\n",
    "    temp = eval(x) if isinstance(x, str) else []\n",
    "    gender = {0:0, 1:0,2:0}\n",
    "    for item in temp:\n",
    "        dic[item['department']]+=1\n",
    "        gender[item['gender']]+=1\n",
    "    dic['gender'] = gender\n",
    "    return dic \n",
    "data['crew_info'] = data['crew'].map(crew_abstract)\n",
    "        \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['crew_gender0'] = data['crew_info'].map(lambda x: x['gender'][0])\n",
    "data['crew_gender1'] = data['crew_info'].map(lambda x: x['gender'][1])\n",
    "data['crew_gender2'] = data['crew_info'].map(lambda x: x['gender'][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Actors',\n",
       " 'Art',\n",
       " 'Camera',\n",
       " 'Costume & Make-Up',\n",
       " 'Crew',\n",
       " 'Directing',\n",
       " 'Editing',\n",
       " 'Lighting',\n",
       " 'Production',\n",
       " 'Sound',\n",
       " 'Visual Effects',\n",
       " 'Writing',\n",
       " 'gender'}"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "department = set()\n",
    "for item in data['crew_info']:\n",
    "    for key in item.keys():\n",
    "        department.add(key)\n",
    "department"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 增加department列，每一列的值为电影对应部门的工作人员数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in department:\n",
    "    data[col] = data['crew_info'].map(lambda x:x[col])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## production"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'name': 'Paramount Pictures', 'id': 4}, {'name': 'United Artists', 'id': 60}, {'name': 'Metro-Goldwyn-Mayer (MGM)', 'id': 8411}]\n",
      "\n",
      "[{'name': 'Walt Disney Pictures', 'id': 2}]\n",
      "\n",
      "[{'name': 'Bold Films', 'id': 2266}, {'name': 'Blumhouse Productions', 'id': 3172}, {'name': 'Right of Way Films', 'id': 32157}]\n",
      "\n",
      "[{'name': 'Ghost House Pictures', 'id': 768}, {'name': 'North Box Productions', 'id': 22637}]\n",
      "\n",
      "[{'name': 'Walt Disney Pictures', 'id': 2}, {'name': 'Jim Henson Productions', 'id': 2504}, {'name': 'Jim Henson Company, The', 'id': 6254}]\n",
      "\n",
      "[{'name': 'Castle Rock Entertainment', 'id': 97}]\n",
      "\n",
      "[{'name': 'United Artists', 'id': 60}]\n",
      "\n",
      "[{'name': 'Twentieth Century Fox Film Corporation', 'id': 306}, {'name': 'Amercent Films', 'id': 5263}, {'name': 'American Entertainment Partners L.P.', 'id': 5264}, {'name': 'Interscope Communications', 'id': 10201}]\n",
      "\n",
      "[{'name': 'DreamWorks SKG', 'id': 27}, {'name': 'Jinks/Cohen Company', 'id': 2721}]\n",
      "\n",
      "[{'name': 'Double Feature Films', 'id': 215}, {'name': 'Jersey Films', 'id': 216}, {'name': 'Nina Saxon Film Design', 'id': 1693}, {'name': 'Metro-Goldwyn-Mayer (MGM)', 'id': 8411}]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# production_companies：电影的出品公司，使用json格式\n",
    "\n",
    "# production_countries: 电影出品公司所在国家，使用json格式\n",
    "print_feat(data, 'production_companies')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'iso_3166_1': 'US', 'name': 'United States of America'}]\n",
      "\n",
      "[{'iso_3166_1': 'US', 'name': 'United States of America'}]\n",
      "\n",
      "[{'iso_3166_1': 'US', 'name': 'United States of America'}]\n",
      "\n",
      "[{'iso_3166_1': 'IN', 'name': 'India'}]\n",
      "\n",
      "[{'iso_3166_1': 'KR', 'name': 'South Korea'}]\n",
      "\n",
      "[{'iso_3166_1': 'US', 'name': 'United States of America'}, {'iso_3166_1': 'CA', 'name': 'Canada'}]\n",
      "\n",
      "[{'iso_3166_1': 'US', 'name': 'United States of America'}]\n",
      "\n",
      "[{'iso_3166_1': 'US', 'name': 'United States of America'}]\n",
      "\n",
      "[{'iso_3166_1': 'US', 'name': 'United States of America'}]\n",
      "\n",
      "[{'iso_3166_1': 'US', 'name': 'United States of America'}]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_feat(data, 'production_countries')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 增加出品公司数目列，表示电影有多少出品公司\n",
    "# 增加所在国家数目列，有些电影是合拍\n",
    "def production_abstract(x):\n",
    "    temp = eval(x) if isinstance(x, str) else []\n",
    "    ans = []\n",
    "    for item in temp:\n",
    "        ans.append(item['name'])\n",
    "    return ans \n",
    "data['company'] = data['production_companies'].map(production_abstract)\n",
    "data['country'] = data['production_countries'].map(production_abstract)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Paramount Pictures': 393,\n",
       "         'United Artists': 105,\n",
       "         'Metro-Goldwyn-Mayer (MGM)': 207,\n",
       "         'Walt Disney Pictures': 147,\n",
       "         'Bold Films': 8,\n",
       "         'Blumhouse Productions': 32,\n",
       "         'Right of Way Films': 5,\n",
       "         'Ghost House Pictures': 11,\n",
       "         'North Box Productions': 1,\n",
       "         'Jim Henson Productions': 3,\n",
       "         'Jim Henson Company, The': 6,\n",
       "         'Castle Rock Entertainment': 44,\n",
       "         'Twentieth Century Fox Film Corporation': 341,\n",
       "         'Amercent Films': 7,\n",
       "         'American Entertainment Partners L.P.': 9,\n",
       "         'Interscope Communications': 20,\n",
       "         'DreamWorks SKG': 78,\n",
       "         'Jinks/Cohen Company': 4,\n",
       "         'Double Feature Films': 14,\n",
       "         'Jersey Films': 16,\n",
       "         'Nina Saxon Film Design': 4,\n",
       "         'Cruise/Wagner Productions': 13,\n",
       "         'Amblin Entertainment': 68,\n",
       "         'Blue Tulip Productions': 3,\n",
       "         'Ronald Shusett/Gary Goldman': 1,\n",
       "         'Digital Image Associates': 3,\n",
       "         'Hypnopolis': 1,\n",
       "         'Craven-Maddalena Films': 5,\n",
       "         'BenderSpink': 14,\n",
       "         'BBC Films': 56,\n",
       "         'Headline Pictures': 2,\n",
       "         'Magnolia Mae Films': 3,\n",
       "         'Taeoo Entertainment': 1,\n",
       "         'UK Film Council': 32,\n",
       "         'Prescience': 8,\n",
       "         'Aegis Film Fund': 5,\n",
       "         'Kaleidoscope Films': 1,\n",
       "         'Current Entertainment': 8,\n",
       "         'TF1 Films Production': 31,\n",
       "         'Canal+': 130,\n",
       "         'TPS Star': 9,\n",
       "         'EuropaCorp': 32,\n",
       "         'Sea Side Films Florida Inc.': 1,\n",
       "         'New Line Cinema': 198,\n",
       "         'Irwin Allen Productions': 3,\n",
       "         'Prelude Pictures': 2,\n",
       "         'Saltire Entertainment': 1,\n",
       "         'Jason Productions': 1,\n",
       "         'The Collective': 2,\n",
       "         'Bloody Disgusting': 3,\n",
       "         '8383 Productions': 3,\n",
       "         '\\xa0Braeburn Entertainment': 1,\n",
       "         '\\xa0Check Entertainment': 2,\n",
       "         'G.C. Pix\\xa0': 1,\n",
       "         'New Zealand Film Commission': 6,\n",
       "         'Live Stock Films': 1,\n",
       "         'Universal Pictures': 463,\n",
       "         'Dark Horse Entertainment': 13,\n",
       "         'Largo Entertainment': 9,\n",
       "         'JVC Entertainment Networks': 6,\n",
       "         'Jean Doumanian Productions': 3,\n",
       "         'Sweetland Films': 1,\n",
       "         'Magnolia Films': 1,\n",
       "         'Jasmine Productions Inc.': 1,\n",
       "         'Fastnet Films': 1,\n",
       "         'Caramel Film': 2,\n",
       "         'No Trace Camping': 2,\n",
       "         'De Laurentiis Entertainment Group (DEG)': 13,\n",
       "         'Marvel Productions': 1,\n",
       "         'Hasbro': 5,\n",
       "         'Sunbow Productions': 1,\n",
       "         'Gaumont': 26,\n",
       "         'CinéCinéma': 15,\n",
       "         'Quad Productions': 4,\n",
       "         'Chaocorp': 3,\n",
       "         'Ten Films': 2,\n",
       "         'TF1': 5,\n",
       "         'Les Films du Losange': 2,\n",
       "         'Wega Film': 2,\n",
       "         'BIM Distribuzione': 8,\n",
       "         'Centre National de la Cinématographie': 5,\n",
       "         'Filmstiftung Nordrhein-Westfalen': 9,\n",
       "         'France 3 Cinéma': 17,\n",
       "         'Eurimages': 15,\n",
       "         'Bavaria Film': 8,\n",
       "         'Studio Canal': 24,\n",
       "         'Arte France Cinéma': 23,\n",
       "         'Westdeutscher Rundfunk (WDR)': 16,\n",
       "         'Filmfonds Wien': 2,\n",
       "         'ORF Film/Fernseh-Abkommen': 1,\n",
       "         'Österreichisches Filminstitut': 1,\n",
       "         'Summit Entertainment': 61,\n",
       "         'Atmosphere Entertainment MM': 7,\n",
       "         'One Race Films': 5,\n",
       "         'Goldmann Pictures': 3,\n",
       "         'NeoReel': 3,\n",
       "         'Aperture Entertainment': 3,\n",
       "         'TIK Films': 4,\n",
       "         'Magnet Releasing': 2,\n",
       "         'Drafthouse Films': 3,\n",
       "         'Timpson Films': 1,\n",
       "         'Lawrence Bender Productions': 9,\n",
       "         'IM Global': 22,\n",
       "         'Automatik Entertainment': 12,\n",
       "         'Trigger Street Productions': 4,\n",
       "         '87Eleven': 4,\n",
       "         'Bórd Scannán na hÉireann': 6,\n",
       "         'British Broadcasting Corporation (BBC)': 10,\n",
       "         'Venus Productions': 1,\n",
       "         'Kopelson Entertainment': 8,\n",
       "         'Punch Productions': 5,\n",
       "         'Warner Bros.': 491,\n",
       "         'Arnold Kopelson Productions': 3,\n",
       "         'Columbia Pictures Corporation': 140,\n",
       "         'Jerry Bresler Productions': 1,\n",
       "         'Rakontur': 1,\n",
       "         'Jalem Productions': 2,\n",
       "         'Lakeshore Entertainment': 36,\n",
       "         'Revelations Entertainment': 7,\n",
       "         'Lascaux Films': 2,\n",
       "         'Latitude Productions': 2,\n",
       "         '26 Films': 4,\n",
       "         'Gold Circle Films': 12,\n",
       "         'Visionview Production': 1,\n",
       "         'Indigo Film': 6,\n",
       "         'Medusa Film': 13,\n",
       "         'Blue Sky Studios': 11,\n",
       "         'Twentieth Century Fox Animation': 13,\n",
       "         'The Weinstein Company': 44,\n",
       "         'FilmColony': 8,\n",
       "         'Ad Hominem Enterprises': 3,\n",
       "         'Walt Disney Productions': 25,\n",
       "         'TriStar Pictures': 121,\n",
       "         'Columbia Pictures': 236,\n",
       "         'Affirm Films': 8,\n",
       "         'LD Entertainment': 8,\n",
       "         'Patrick Aiello Productions': 1,\n",
       "         'Hollywood Pictures': 55,\n",
       "         'Mon Voisin Productions': 2,\n",
       "         'PolyGram Filmed Entertainment': 36,\n",
       "         'Speak Productions': 1,\n",
       "         'Eon Productions': 22,\n",
       "         'Stillking Films': 11,\n",
       "         'Babelsberg Film': 4,\n",
       "         'Joanna Productions': 1,\n",
       "         'Atlas Entertainment': 16,\n",
       "         'Turner Pictures (I)': 2,\n",
       "         'Sean S. Cunningham Films': 8,\n",
       "         'Horror Inc.': 1,\n",
       "         'Paramount Vantage': 13,\n",
       "         'Bona Fide Productions': 6,\n",
       "         'Cannon Films': 6,\n",
       "         'Company Films': 3,\n",
       "         'Moving Pictures Film and Television': 1,\n",
       "         'Office Kitano': 2,\n",
       "         'Xstream Pictures': 1,\n",
       "         'Shanghai Film Group': 7,\n",
       "         'Section Eight': 8,\n",
       "         'Mandalay Pictures': 9,\n",
       "         'VIP 3 Medienfonds': 2,\n",
       "         '2929 Productions': 12,\n",
       "         'Rising Star': 8,\n",
       "         'VIP 2 Medienfonds': 3,\n",
       "         'Warner Independent Pictures (WIP)': 8,\n",
       "         'Senator Film Produktion': 9,\n",
       "         'Wildwood Enterprises': 13,\n",
       "         'Sound for Film': 3,\n",
       "         'Inca Films S.A.': 1,\n",
       "         'Filmfour': 5,\n",
       "         'BD Cine': 2,\n",
       "         'Tu Vas Voir Productions': 1,\n",
       "         'Sahara Films': 1,\n",
       "         'Imagine Entertainment': 48,\n",
       "         'New World Pictures': 16,\n",
       "         'Touchstone Pictures': 158,\n",
       "         'Orion Pictures': 56,\n",
       "         'Deliverance Productions': 2,\n",
       "         'Spectacle Entertainment Group': 4,\n",
       "         'Windy Hill Pictures': 2,\n",
       "         'Protagonist Pictures': 3,\n",
       "         'Bow and Arrow Entertainment': 1,\n",
       "         'PalmStar Media': 8,\n",
       "         'Intermedia Films': 23,\n",
       "         'IM Filmproduktion': 1,\n",
       "         'Cosmic Pictures': 1,\n",
       "         'Alphaville Films': 13,\n",
       "         'Epsilon Motion Pictures': 37,\n",
       "         'Hyde Park Films': 7,\n",
       "         'Shopgirl': 1,\n",
       "         'MFPV Film': 2,\n",
       "         'ShadowCatcher Entertainment': 1,\n",
       "         'Daniel Bobker Productions': 1,\n",
       "         'Brick Dust Productions LLC': 1,\n",
       "         'Elle Driver': 2,\n",
       "         'Tazora Films': 1,\n",
       "         'Kennedy/Marshall Company, The': 14,\n",
       "         'Golden Harvest Company': 16,\n",
       "         'Scion Films': 15,\n",
       "         'Czech Anglo Productions': 2,\n",
       "         'Canal Plus': 14,\n",
       "         'Regency Enterprises': 81,\n",
       "         'Alcor Films': 10,\n",
       "         'Miramax Films': 104,\n",
       "         'A Band Apart': 7,\n",
       "         'Super Cool ManChu': 2,\n",
       "         'R%26C Produzioni': 1,\n",
       "         'Les Films Balenciaga': 1,\n",
       "         'France 2 Cinéma': 39,\n",
       "         'Hachette Première': 2,\n",
       "         'Blast! Films': 1,\n",
       "         'Asymmetrical Productions': 3,\n",
       "         'Les Films Alain Sarde': 4,\n",
       "         'Babbo Inc.': 1,\n",
       "         'The Picture Factory': 1,\n",
       "         'Yellow, Black & White': 2,\n",
       "         'Procirep': 7,\n",
       "         'Film Fund Luxembourg': 3,\n",
       "         'Centre National de la Cinématographie (CNC)': 28,\n",
       "         'Media Programme of the European Community': 6,\n",
       "         'Ciné+': 34,\n",
       "         'Ren Film': 1,\n",
       "         'Epic Productions': 4,\n",
       "         'Vision International': 2,\n",
       "         'Vision PDG': 3,\n",
       "         'Trans World Entertainment (TWE)': 1,\n",
       "         'Barunson Film Division': 2,\n",
       "         'Film It Suda': 3,\n",
       "         'Sio Film and Bravo Entertainment': 1,\n",
       "         'Gran Via Productions': 8,\n",
       "         'Walden Media': 20,\n",
       "         'Lemodeln Model & Talent Agency': 1,\n",
       "         'Çamaşırhane': 1,\n",
       "         'Kinostar': 1,\n",
       "         'Tiglon': 1,\n",
       "         'Emrah Gamsızoğlu': 1,\n",
       "         'Isle of Man Film': 8,\n",
       "         'DJ Films': 2,\n",
       "         'British Film Institute (BFI)': 12,\n",
       "         'Pinewood Studios': 2,\n",
       "         'Metrol Technology': 4,\n",
       "         'CBS Films': 15,\n",
       "         'Icon Entertainment International': 15,\n",
       "         'The Ladd Company': 13,\n",
       "         'B.H. Finance C.V.': 1,\n",
       "         'Pyramide Productions': 3,\n",
       "         'The Mirisch Corporation': 6,\n",
       "         'Cheyenne Enterprises': 9,\n",
       "         'Empire Pictures': 6,\n",
       "         'Hyde Park Entertainment': 8,\n",
       "         'Baltimore Spring Creek Productions': 4,\n",
       "         'Lotus Pictures': 1,\n",
       "         'Revolution Studios': 32,\n",
       "         'Blue Star Pictures': 3,\n",
       "         'Spring Creek Productions': 9,\n",
       "         'Roth Films': 9,\n",
       "         'K. JAM Media': 1,\n",
       "         'Cott Productions': 2,\n",
       "         'Surf Film': 1,\n",
       "         'Enelmar Productions, A.I.E.': 1,\n",
       "         'Rastar Pictures': 5,\n",
       "         'WingNut Films': 13,\n",
       "         'The Saul Zaentz Company': 5,\n",
       "         'Emperor Motion Pictures': 7,\n",
       "         'Media Asia Films': 9,\n",
       "         'Sil-Metropole Organisation Ltd.': 2,\n",
       "         'Huayi Brothers Media': 6,\n",
       "         'China Film Co.': 4,\n",
       "         'Chongoing Film Group': 1,\n",
       "         'Bon Voyage Film Studio': 1,\n",
       "         'Shanghai Media Group': 1,\n",
       "         'Zhejiang Films & TV(Group) Company Ltd.': 1,\n",
       "         'Hunan Broadcasting System': 1,\n",
       "         'Anhui Broadcasting Corp.': 1,\n",
       "         'Beijing TV Station': 1,\n",
       "         'Wild Hogs Productions': 1,\n",
       "         'Mainline Pictures': 1,\n",
       "         'Silver Pictures': 42,\n",
       "         'Fox Searchlight Pictures': 69,\n",
       "         'Watermark': 1,\n",
       "         'Dune Entertainment III': 27,\n",
       "         'Australian Film Finance Corporation': 3,\n",
       "         'Zero Fiction Film': 1,\n",
       "         'Ming Productions': 1,\n",
       "         'Bluewater Pictures': 1,\n",
       "         'Cheerland Entertainment Organization': 1,\n",
       "         'Debra Hill Productions': 3,\n",
       "         '40 Acres & A Mule Filmworks': 16,\n",
       "         'StudioCanal': 65,\n",
       "         'Baby Cow Productions': 2,\n",
       "         'Baby Cow Films': 1,\n",
       "         'Discovery Channel Pictures': 2,\n",
       "         'Legendary Pictures': 40,\n",
       "         'Green Hat Films': 6,\n",
       "         'Entertainment Films': 1,\n",
       "         'Material Entertainment': 1,\n",
       "         'Beech Hill Films': 1,\n",
       "         'Fandango': 4,\n",
       "         'Vertigo Productions': 2,\n",
       "         'SBS Independent': 1,\n",
       "         'Adelaide Film Festival': 1,\n",
       "         'Paul Schiff Productions': 3,\n",
       "         'ArieScope Pictures': 2,\n",
       "         'A Bigger Boat': 3,\n",
       "         \"Donners' Company\": 16,\n",
       "         'Bad Hat Harry Productions': 12,\n",
       "         'Marvel Enterprises': 19,\n",
       "         'XM2 Productions': 1,\n",
       "         'XF2 Canada Productions': 1,\n",
       "         'Robert Wise Productions': 3,\n",
       "         'Wild Bear Films': 1,\n",
       "         'Archer Street Productions': 2,\n",
       "         'Delux Productions': 2,\n",
       "         'Duplass Brothers Productions': 7,\n",
       "         'Senator Entertainment Co': 1,\n",
       "         'Killer Films': 14,\n",
       "         'Parts and Labor': 6,\n",
       "         'FullDawa Films': 1,\n",
       "         'CJ Entertainment': 16,\n",
       "         'Opus Pictures': 2,\n",
       "         'SnowPiercer': 1,\n",
       "         'Moho Film': 2,\n",
       "         'Northern Lights Entertainment': 6,\n",
       "         'Social Capital': 2,\n",
       "         'Film Afrika Worldwide': 3,\n",
       "         'Lipsync Productions': 13,\n",
       "         'Procinvest Sas': 2,\n",
       "         'Cinedigm': 3,\n",
       "         'Magnet Media Productions': 2,\n",
       "         'Magnet Media Group': 2,\n",
       "         'Mirabelle Pictures': 1,\n",
       "         'Alliance Cinema': 1,\n",
       "         'Windwalker': 1,\n",
       "         'Santa Fe International': 1,\n",
       "         'Whitewater Films': 4,\n",
       "         'Very Special Projects': 2,\n",
       "         'WRA Productions': 1,\n",
       "         'Nuyorican Productions': 1,\n",
       "         'Smart Entertainment': 2,\n",
       "         'Magnum Motion Pictures Inc..': 1,\n",
       "         'Walt Disney Television Animation': 3,\n",
       "         'Walt Disney Animation Australia': 2,\n",
       "         'DisneyToon Studios': 4,\n",
       "         'Walt Disney Animation Canada': 1,\n",
       "         'Global Pictures': 1,\n",
       "         'Franchise Pictures': 19,\n",
       "         'Morgan Creek Productions': 40,\n",
       "         'The Canton Company': 3,\n",
       "         'Yari Film Group': 11,\n",
       "         'Stratus Film Co.': 2,\n",
       "         'Jascat': 1,\n",
       "         'Ada Films': 1,\n",
       "         'Village Roadshow Pictures': 89,\n",
       "         'Dark Castle Entertainment': 16,\n",
       "         'Eyetronics': 2,\n",
       "         'Chime Films': 1,\n",
       "         'IRE Productions': 3,\n",
       "         'Santa Fe Institute for Regional Education': 3,\n",
       "         'Firm Films': 7,\n",
       "         'Screen Gems': 39,\n",
       "         'Jewel Productions': 2,\n",
       "         'Pimlico Films': 2,\n",
       "         'Incorporated Television Company (ITC)': 3,\n",
       "         'Kennedy Miller Productions': 10,\n",
       "         'Filmhaus': 1,\n",
       "         'Price Entertainment': 2,\n",
       "         'Parabolic Pictures': 3,\n",
       "         'Stable Way Entertainment': 2,\n",
       "         'Quickfire Films': 6,\n",
       "         'Screen Australia': 12,\n",
       "         'Screen NSW': 4,\n",
       "         'Ingenious Broadcasting': 1,\n",
       "         'Unthank Films': 1,\n",
       "         'Story Bridge Films': 1,\n",
       "         'Auburn Entertainment': 1,\n",
       "         'Original Film': 35,\n",
       "         'Ardustry Entertainment': 2,\n",
       "         'Mediastream Film GmbH & Co. Productions KG': 1,\n",
       "         'Hammerhead Productions': 2,\n",
       "         'Screenland Pictures': 1,\n",
       "         'Stand See': 1,\n",
       "         'Solaris Film': 2,\n",
       "         'Avery Pix': 5,\n",
       "         \"O'Connor Brothers\": 1,\n",
       "         'Kumar Mobiliengesellschaft mbH & Co. Projekt Nr. 1 KG': 3,\n",
       "         'Film Council': 3,\n",
       "         'Scottish Screen': 4,\n",
       "         'Hughes Entertainment': 10,\n",
       "         'Toho Company': 16,\n",
       "         'Sedic International': 2,\n",
       "         'Happy Madison Productions': 30,\n",
       "         'Steve White Entertainment': 1,\n",
       "         'Alliance Films': 14,\n",
       "         'Pixar Animation Studios': 18,\n",
       "         'FortyFour Studios': 1,\n",
       "         'Lionsgate': 68,\n",
       "         '34th Street Films': 1,\n",
       "         'Dimension Films': 60,\n",
       "         'Trans Atlantic Entertainment': 2,\n",
       "         'Alcon Entertainment': 21,\n",
       "         'Phoenix Pictures': 17,\n",
       "         'What to Expect Productions': 1,\n",
       "         'British Film Company': 1,\n",
       "         'Cannon Group': 17,\n",
       "         'Werc Werk Works': 3,\n",
       "         'Majid Majidi Film Production': 1,\n",
       "         'Smart Egg Pictures': 6,\n",
       "         'Dune Entertainment': 62,\n",
       "         'Major Studio Partners': 7,\n",
       "         'New Regency Pictures': 46,\n",
       "         'Sentinel Productions': 1,\n",
       "         'Furthur Films': 4,\n",
       "         'Bel Air Entertainment': 6,\n",
       "         'Chernin Entertainment': 14,\n",
       "         'Kerner Entertainment Company': 4,\n",
       "         'Kestrel Films': 1,\n",
       "         'Earth Girls': 1,\n",
       "         'Reel FX Creative Studios': 4,\n",
       "         'Relativity Media': 115,\n",
       "         'Gracie Films': 10,\n",
       "         'Night Light Films': 1,\n",
       "         'Itaca Films': 1,\n",
       "         'BN Films': 1,\n",
       "         'Brightside Entertainment': 1,\n",
       "         '1019 Entertainment': 1,\n",
       "         'Yoruba Saxon Productions': 1,\n",
       "         'IRS Media': 1,\n",
       "         'Saban Entertainment': 2,\n",
       "         'Toei Company': 4,\n",
       "         'Admire Productions Ltd.': 1,\n",
       "         'Coral Productions': 1,\n",
       "         'Triumph Films': 5,\n",
       "         'Casey Silver Productions': 5,\n",
       "         'Dune Films': 7,\n",
       "         'Cometstone Pictures': 1,\n",
       "         'Zanuck/Brown Productions': 5,\n",
       "         'SLB Films Pvt. Ltd.': 2,\n",
       "         'Bleiberg Entertainment': 2,\n",
       "         'Millennium Films': 34,\n",
       "         'Stuber Productions': 7,\n",
       "         'Aggregate Films': 1,\n",
       "         'DumbDumb': 1,\n",
       "         'Toho': 6,\n",
       "         'Columbia Pictures Industries': 1,\n",
       "         'Europa Film': 1,\n",
       "         'Viking Films': 2,\n",
       "         'Rifilm': 1,\n",
       "         'China Film Group Corporation (CFGC)': 6,\n",
       "         'Stellar Megamedia': 1,\n",
       "         'Jiangsu Broadcasting System': 1,\n",
       "         'Chuan Production Film Studio': 1,\n",
       "         'Penta Pictures': 2,\n",
       "         'Carolco Pictures': 22,\n",
       "         'Apostle': 1,\n",
       "         'Type 55 Films': 1,\n",
       "         'The Australian Film Commission': 6,\n",
       "         'Endymion Films': 1,\n",
       "         'Australian Film Finance Corporation (AFFC)': 6,\n",
       "         'Working Title Films': 63,\n",
       "         'WTA': 1,\n",
       "         'Woss Group Film Productions': 1,\n",
       "         'Saturn Films': 16,\n",
       "         'Nu Image Films': 18,\n",
       "         'Michael De Luca Productions': 10,\n",
       "         'Warner Brothers/Seven Arts': 3,\n",
       "         'Merchant Ivory Productions': 8,\n",
       "         'Studio Babelsberg': 29,\n",
       "         'Mostow/Lieberman Productions': 2,\n",
       "         '80 Days Productions': 1,\n",
       "         'Spanknyce Films': 1,\n",
       "         'Fitzwilliam Productions': 1,\n",
       "         'Ruby in Paradise': 1,\n",
       "         'Full Crew/Say Yea': 1,\n",
       "         'Le Bureau': 2,\n",
       "         'Film4': 49,\n",
       "         'Free Range Films': 3,\n",
       "         'Plan B Entertainment': 16,\n",
       "         'Northern Ireland Screen': 2,\n",
       "         'Sierra / Affinity': 2,\n",
       "         'MICA Entertainment': 4,\n",
       "         'MadRiver Pictures': 2,\n",
       "         'Keep Your Head': 2,\n",
       "         'Michael Phillips Productions': 1,\n",
       "         'Tales From The Crypt Holdings': 1,\n",
       "         'Universal City Studios': 3,\n",
       "         'Impala': 1,\n",
       "         'In-Cine Compania Industrial Cinematografica': 2,\n",
       "         'Fox 2000 Pictures': 52,\n",
       "         'Kouf/Bigelow Productions': 3,\n",
       "         'Prana Animation Studios': 3,\n",
       "         'Summertime Entertainment': 1,\n",
       "         'Madhouse': 5,\n",
       "         'Warner Bros. Japan': 1,\n",
       "         'Egg Pictures': 3,\n",
       "         'Melvin Simon Productions': 4,\n",
       "         'American International Pictures (AIP)': 5,\n",
       "         'Alloy Entertainment': 2,\n",
       "         'Goldcrest Pictures': 10,\n",
       "         'Degeto Film': 2,\n",
       "         'EOS Entertainment': 2,\n",
       "         'Rai Cinema': 11,\n",
       "         'Österreichischer Rundfunk (ORF)': 3,\n",
       "         'Constantin Film Produktion': 20,\n",
       "         'Norddeutscher Rundfunk (NDR)': 4,\n",
       "         'NPV Entertainment': 22,\n",
       "         'Jonathan Krane Group': 1,\n",
       "         'EMI Films Ltd.': 6,\n",
       "         'Mersham Productions': 1,\n",
       "         'TPS Cinéma': 3,\n",
       "         'M6 Métropole Télévision': 1,\n",
       "         'Télégraphe': 1,\n",
       "         'Vertigo': 2,\n",
       "         'Le Studio Canal+': 3,\n",
       "         'Laurence Mark Productions': 8,\n",
       "         'Broken Lizard Industries': 3,\n",
       "         'Coconut Pete Productions': 1,\n",
       "         'APT Entertainment': 1,\n",
       "         'Star Cinema Productions': 4,\n",
       "         'Cinemalaya Foundation': 1,\n",
       "         'Darko Entertainment': 6,\n",
       "         'Aggregate Film': 1,\n",
       "         'Cross Creek Pictures': 12,\n",
       "         'Free State Pictures': 3,\n",
       "         'RVK Studios': 1,\n",
       "         'Bandai Visual Company': 7,\n",
       "         'Kodansha': 4,\n",
       "         'Production I.G.': 5,\n",
       "         'Red Wagon Entertainment': 9,\n",
       "         'IMF Internationale Medien und Film GmbH & Co. 3. Produktions KG': 6,\n",
       "         'RV Camping Productions Ltd.': 1,\n",
       "         'Asmik Ace Entertainment': 2,\n",
       "         'Fuji Television Network': 12,\n",
       "         'WoWow': 3,\n",
       "         'Dentsu': 18,\n",
       "         'Sumitomo Corporation': 2,\n",
       "         'Sankei Shimbun': 1,\n",
       "         'ERP Productions': 1,\n",
       "         'River Road Entertainment': 9,\n",
       "         'Art Linson Productions': 6,\n",
       "         'Into the Wild': 1,\n",
       "         'View Askew Productions': 8,\n",
       "         'Weinstein Company, The': 5,\n",
       "         'Samuelson Productions': 1,\n",
       "         'Color Force': 8,\n",
       "         'Apatow Productions': 21,\n",
       "         'Steamroller Productions': 1,\n",
       "         'Victor & Grais Productions': 2,\n",
       "         'Universal Pictures International (UPI)': 3,\n",
       "         'Jigsaw': 1,\n",
       "         'Global Produce': 2,\n",
       "         'Caravan Pictures': 14,\n",
       "         'Roger Birnbaum Productions': 4,\n",
       "         'Rabbit Bandini Films': 1,\n",
       "         'Great Point Media': 2,\n",
       "         'Parkes/MacDonald Productions': 7,\n",
       "         'Edge City': 2,\n",
       "         'Destination Films': 12,\n",
       "         'Mutant Enemy Productions': 1,\n",
       "         'Ce Qui Me Meut Motion Pictures': 2,\n",
       "         'StudioCanal Image': 1,\n",
       "         'Uni Etoile 4': 1,\n",
       "         'Emmett/Furla Films': 20,\n",
       "         'Exclusive Media Group': 15,\n",
       "         'Hedge Fund Film Partners': 2,\n",
       "         'Crave Films': 3,\n",
       "         'Knightsbridge Entertainment': 4,\n",
       "         'Le Grisbi Productions': 4,\n",
       "         '5150 Action': 4,\n",
       "         'Goldcrest Films International': 12,\n",
       "         'Peter Newman/Interal': 1,\n",
       "         'Mary Breen-Farrelly Productions': 1,\n",
       "         'Irish Film Industry': 1,\n",
       "         'Pachyderm Production': 1,\n",
       "         'Blinding Edge Pictures': 9,\n",
       "         'Savoy Pictures': 7,\n",
       "         'DreamWorks Pictures': 10,\n",
       "         'Don Bluth Productions': 3,\n",
       "         'Channel Four Films': 21,\n",
       "         'Spyglass Entertainment': 37,\n",
       "         '3 Arts Entertainment': 10,\n",
       "         'Jolie Pas': 2,\n",
       "         'Columbia TriStar': 2,\n",
       "         'Art Pictures Studio': 5,\n",
       "         'Shady Acres Entertainment': 4,\n",
       "         'Kalima Productions GmbH & Co. KG': 4,\n",
       "         'NDE Productions': 1,\n",
       "         'Silver Screen Partners IV': 13,\n",
       "         'Hemdale Film Corporation': 7,\n",
       "         'No Frills Film Production': 2,\n",
       "         'Golan-Globus Productions': 13,\n",
       "         'The Cannon Group': 2,\n",
       "         'Initial Entertainment Group (IEG)': 9,\n",
       "         'Alberto Grimaldi Productions': 1,\n",
       "         'Playtone': 14,\n",
       "         'Wild Things Productions': 1,\n",
       "         'Appian Way': 12,\n",
       "         'Pearl Street Films': 4,\n",
       "         'Gunn Films': 5,\n",
       "         'Casual Friday Productions': 1,\n",
       "         'Montecito Picture Company, The': 6,\n",
       "         'Outpost Studios': 1,\n",
       "         'MK2 Productions': 7,\n",
       "         'Cookout Productions': 1,\n",
       "         'Malpaso Productions': 31,\n",
       "         'Aardman Animations': 5,\n",
       "         'Sony Pictures Animation': 13,\n",
       "         'Media Rights Capital': 14,\n",
       "         'Sony Pictures Entertainment (SPE)': 11,\n",
       "         'QED International': 10,\n",
       "         'Alpha Core': 2,\n",
       "         'Genre Films': 6,\n",
       "         'Simon Kinberg Productions': 2,\n",
       "         'The Rank Organisation': 4,\n",
       "         'Enigma Productions': 5,\n",
       "         'National Film Finance Corporation (NFFC)': 3,\n",
       "         'Misher Films': 6,\n",
       "         'Yucaipa Films': 3,\n",
       "         'Rat Entertainment': 5,\n",
       "         'Misha Films': 1,\n",
       "         'CatchPlay': 4,\n",
       "         'Anonymous Content': 20,\n",
       "         'Hong Kong Alpha Motion Pictures Co.': 1,\n",
       "         'RatPac Entertainment': 7,\n",
       "         'M Productions': 2,\n",
       "         'Monarchy Enterprises S.a.r.l.': 3,\n",
       "         'Joseph M. Singer Entertainment': 2,\n",
       "         'Davis Entertainment': 37,\n",
       "         'Scott Rudin Productions': 40,\n",
       "         'Grive Productions': 7,\n",
       "         'A.J.O.Z. Films': 2,\n",
       "         'Maguire Entertainment': 2,\n",
       "         'Sony Pictures Home Entertainment': 2,\n",
       "         'Animal Logic': 5,\n",
       "         'AVCO Embassy Pictures': 9,\n",
       "         'Guardian Trust Company': 3,\n",
       "         'RSL Entertainment Corp.': 1,\n",
       "         'Moviecorp VI': 1,\n",
       "         'China Film Group Corporation': 1,\n",
       "         'Greenestreet Films': 5,\n",
       "         'Eclipse Catering': 2,\n",
       "         'K/O Paper Products': 5,\n",
       "         'SOIXAN7E QUIN5E': 1,\n",
       "         'See Me Louisiana': 1,\n",
       "         'Milkshake Films': 1,\n",
       "         'Hargitay & Hargitay Pictures in Motion': 1,\n",
       "         'Tamm Productions': 1,\n",
       "         'Rysher Entertainment': 8,\n",
       "         'Here Films': 1,\n",
       "         'Central Motion Pictures': 3,\n",
       "         'Fauna Productions': 1,\n",
       "         'Di Bonaventura Pictures': 20,\n",
       "         'Mace Neufeld Productions': 11,\n",
       "         'Skydance Productions': 11,\n",
       "         'Etalon film': 2,\n",
       "         'Buckaroo Entertainment': 4,\n",
       "         'Likely Story': 12,\n",
       "         'Overbrook Entertainment': 13,\n",
       "         'Allied Filmmakers': 8,\n",
       "         'Topcraft': 2,\n",
       "         'HBO/Cinemax Documentary': 1,\n",
       "         'thinkfilm': 3,\n",
       "         'Creative Visions Productions': 1,\n",
       "         'Red Light Films': 1,\n",
       "         'Sundance Institute Documentary Fund': 1,\n",
       "         'Participant Media': 36,\n",
       "         'Closest to the Hole Productions': 7,\n",
       "         'Leverage Entertainment': 4,\n",
       "         'TIK Film': 1,\n",
       "         'Danjaq': 8,\n",
       "         'MHF Zweite Academy Film': 4,\n",
       "         'Marvel Knights': 2,\n",
       "         'Valhalla Motion Pictures': 8,\n",
       "         'SGF Entertainment': 1,\n",
       "         'Muse Entertainment': 1,\n",
       "         'Meteor 17': 1,\n",
       "         'Crew Neck Productions': 1,\n",
       "         'Bristol Bay Productions': 3,\n",
       "         'Baldwin Entertainment Group': 4,\n",
       "         'Desertlands Entertainment': 1,\n",
       "         'Kanzaman': 8,\n",
       "         'J.K. Livin Productions': 1,\n",
       "         'Moguletta': 1,\n",
       "         'Sahara Productions': 2,\n",
       "         'Walt Disney Feature Animation': 11,\n",
       "         'Impact Pictures': 13,\n",
       "         'Jerry Weintraub Productions': 11,\n",
       "         'Key Creatives': 2,\n",
       "         'New Zealand Large Budget Screen Production Grant': 1,\n",
       "         'Kissaki Films': 1,\n",
       "         'Stacey Reiss Productions': 1,\n",
       "         'Reliance Big Pictures': 2,\n",
       "         'Studio Green': 5,\n",
       "         'Star Media': 1,\n",
       "         'Интерфест': 1,\n",
       "         'Реал-Дакота': 1,\n",
       "         'Why Not Productions': 17,\n",
       "         'Les Films Du Fleuve': 6,\n",
       "         'Vlaams Audiovisueel fonds': 2,\n",
       "         'Radio Télévision Belge Francophone (RTBF)': 6,\n",
       "         'Page 114': 2,\n",
       "         'Lumière': 1,\n",
       "         'Lunanime': 1,\n",
       "         'France Télévisions': 12,\n",
       "         \"Centre du Cinéma et de l'Audiovisuel de la Fédération Wallonie-Bruxelles\": 4,\n",
       "         'VOO': 4,\n",
       "         \"Région Provence-Alpes-Côte d'Azur\": 2,\n",
       "         'Département des Alpes-Maritimes': 1,\n",
       "         'Casa Kafka Pictures Movie Tax Shelter Empowered by Dexia': 1,\n",
       "         'Forward Pass': 9,\n",
       "         'Kaitz Productions': 1,\n",
       "         'Mann/Roth Productions': 1,\n",
       "         'Thinkfilm': 4,\n",
       "         'Cecchi Gori Group Tiger Cinematografica': 4,\n",
       "         'AMLF': 2,\n",
       "         'Bavaria Entertainment': 1,\n",
       "         'Round Films': 1,\n",
       "         'Rachael Horovitz Productions': 1,\n",
       "         'Chapter One Films': 3,\n",
       "         'UTV Motion Pictures': 15,\n",
       "         'Double Play': 1,\n",
       "         'Geffen Company, The': 3,\n",
       "         'Arachnid Productions Ltd.': 1,\n",
       "         'Outland Productions': 1,\n",
       "         'Nickelodeon Movies': 19,\n",
       "         'Klasky-Csupo': 1,\n",
       "         'Circle Films': 3,\n",
       "         'Vanguard Films': 4,\n",
       "         'Scanbox': 1,\n",
       "         'New Real Films': 2,\n",
       "         'Lumanity Production': 1,\n",
       "         'Bedford Falls Productions': 5,\n",
       "         'Lonely Film Productions GmbH & Co. KG.': 4,\n",
       "         'Virtual Studios': 7,\n",
       "         'Liberty Pictures': 1,\n",
       "         'Pathé Renn Productions': 8,\n",
       "         'Pathe': 6,\n",
       "         'Two Brothers Productions': 1,\n",
       "         'Propaganda Films': 12,\n",
       "         'Focus Features': 45,\n",
       "         'Ixtlan': 4,\n",
       "         'Tiger Aspect Productions': 3,\n",
       "         'Grindstone Entertainment Group': 8,\n",
       "         'Cheetah Vision': 4,\n",
       "         'Paradox Entertainment': 6,\n",
       "         'Gunny Entertainment': 1,\n",
       "         'Colorado Film Production': 2,\n",
       "         'Bazelevs Production': 13,\n",
       "         'Tabo Tabo Films': 1,\n",
       "         'Radio Télévision Suisse (RTS)': 1,\n",
       "         'SRG SSR idée suisse': 1,\n",
       "         'Office Fédéral de la Culture': 1,\n",
       "         'Loterie Suisse Romande': 1,\n",
       "         'Bande a Part Films': 1,\n",
       "         'Sampek Productions': 1,\n",
       "         'SofiTVCiné 3': 1,\n",
       "         'Cofimage 27': 1,\n",
       "         'Diligence Films': 1,\n",
       "         'Cinéforom': 1,\n",
       "         '1492 Pictures': 17,\n",
       "         'Eurasia Investments': 1,\n",
       "         'Hartbeat Productions': 1,\n",
       "         'Allied Vision': 2,\n",
       "         'The Picture Property Company': 1,\n",
       "         'Pheasantry Films': 1,\n",
       "         'Polar Entertainment': 2,\n",
       "         'Team Todd': 8,\n",
       "         'Ubu Productions': 1,\n",
       "         'Fuzzy Logic Pictures': 1,\n",
       "         'Strongman': 1,\n",
       "         'Wigwam Films': 1,\n",
       "         'Pitchblack Pictures Inc.': 1,\n",
       "         'Lighthouse Pictures': 1,\n",
       "         'Caliber Media Company': 1,\n",
       "         'Motion Picture Corporation of America (MPCA)': 2,\n",
       "         'Mandalay Entertainment': 7,\n",
       "         'Summer Knowledge LLC': 2,\n",
       "         'Paramount Animation': 9,\n",
       "         'Illusion Entertainment Group': 2,\n",
       "         'Clyde Is Hungry Films': 2,\n",
       "         'Canal+ Droits Audiovisuels': 4,\n",
       "         'Knickerbocker Films': 2,\n",
       "         'SLM Production Group': 13,\n",
       "         'Brandywine Productions': 8,\n",
       "         'The Kennedy/Marshall Company': 10,\n",
       "         'Temple Hill Productions': 1,\n",
       "         'Revolver Film': 1,\n",
       "         'Waterland Film & TV': 1,\n",
       "         'Max TV': 2,\n",
       "         'MFP Munich Film Partners GmbH & Company I. Produktions KG': 4,\n",
       "         'Krane Entertainment': 3,\n",
       "         'Neufeld Rehme Productions': 1,\n",
       "         'Tokyo Broadcasting System (TBS)': 5,\n",
       "         'Amuse Soft Entertainment': 2,\n",
       "         'Mainichi Broadcasting System (MBS)': 5,\n",
       "         'Shochiku Company': 3,\n",
       "         'Sedic': 2,\n",
       "         'Shogakukan': 4,\n",
       "         'Asahi Shimbun': 1,\n",
       "         'TBS Radio & Communications': 1,\n",
       "         'Racing Pictures': 1,\n",
       "         'Antidote Films (I)': 3,\n",
       "         'Wide Frame Pictures': 1,\n",
       "         'Panorama Studios': 1,\n",
       "         'Gravier Productions': 7,\n",
       "         'Televisió de Catalunya (TV3)': 5,\n",
       "         'Versátil Cinema': 2,\n",
       "         'Mediapro': 5,\n",
       "         'Pontchartrain Productions': 1,\n",
       "         'Six Point Harness': 1,\n",
       "         'Ars Nova': 1,\n",
       "         'Goliath Entertainment': 1,\n",
       "         'Pandora Filmproduktion': 7,\n",
       "         'Telespan 2000': 3,\n",
       "         'Antena 3 Films': 5,\n",
       "         'Etb (Euskal Telebista)': 1,\n",
       "         'Sayaka Producciones Audiovisuales': 2,\n",
       "         'Canal+ España': 23,\n",
       "         'Vertice 360': 1,\n",
       "         'Australian Film Commission': 1,\n",
       "         'Syncopy': 10,\n",
       "         'Annapurna Pictures': 13,\n",
       "         'FilmEngine': 6,\n",
       "         'Katalyst Films': 4,\n",
       "         'Province of British Columbia Production Services Tax Credit': 8,\n",
       "         'Miracle Pictures': 3,\n",
       "         'Palomar Pictures (II)': 4,\n",
       "         'Manifest Film Company': 3,\n",
       "         'Newmarket Capital Group': 10,\n",
       "         'Pistolero Productions LLC': 1,\n",
       "         'Polish Brothers Construction': 1,\n",
       "         'Brothers K Productions': 1,\n",
       "         'The Mount Company': 3,\n",
       "         'SBS Productions': 4,\n",
       "         'Globo filmes': 3,\n",
       "         'CinemaScópio Produções': 1,\n",
       "         'Hollywood Gang Productions': 2,\n",
       "         'Nimar Studios': 5,\n",
       "         'Cruel and Unusual Films': 7,\n",
       "         'Serendipity Point Films': 4,\n",
       "         'Egoli Tossell Film AG': 6,\n",
       "         'A24': 33,\n",
       "         'David Foster Productions': 6,\n",
       "         'Turman-Foster Company': 2,\n",
       "         'Les Productions du Trésor': 6,\n",
       "         'C-Films AG': 1,\n",
       "         'Peter McCarthy / Front Films': 1,\n",
       "         'Pacific Arts Video': 1,\n",
       "         'Foresight Unlimited': 7,\n",
       "         'Baumgarten Management and Productions (BMP)': 3,\n",
       "         'Signature Entertainment': 5,\n",
       "         'Unisol 3 Distribution': 1,\n",
       "         'Beacon Communications': 8,\n",
       "         'Radiant Productions': 5,\n",
       "         'Dante Entertainment': 2,\n",
       "         'Crusader Entertainment': 1,\n",
       "         'ETIC Films': 4,\n",
       "         'Forge': 3,\n",
       "         'QI Quality International GmbH Co. KG': 1,\n",
       "         'Signature Pictures': 8,\n",
       "         'ApolloMedia Distribution': 6,\n",
       "         'Coco': 1,\n",
       "         'Film 111': 1,\n",
       "         'Jericho Productions Ltd.': 1,\n",
       "         'MFF (Sound of Thunder)': 1,\n",
       "         'Matrix Film Finance': 1,\n",
       "         'Scenario Lane Productions': 1,\n",
       "         'C.R.G. International': 1,\n",
       "         'Trademark Films': 2,\n",
       "         'Micro Fusion 2003-2': 1,\n",
       "         'Remark Films': 1,\n",
       "         'City on a Hill Productions': 1,\n",
       "         'Fifth Avenue Entertainment': 2,\n",
       "         'Lakeshore International': 1,\n",
       "         'Nordisk Film': 8,\n",
       "         'Ingenious Film Partners': 27,\n",
       "         'Capitol Films': 7,\n",
       "         'Potboiler Productions Ltd.': 1,\n",
       "         'Northern Ireland Film and Television Commission': 1,\n",
       "         'Nepenthe Productions': 2,\n",
       "         'Irish Film Board': 16,\n",
       "         'Treasure Entertainment': 1,\n",
       "         'Selznick International Pictures': 3,\n",
       "         'HBO Films': 8,\n",
       "         'Los Hooligans Productions': 3,\n",
       "         'Fons Rademakers Produktie': 1,\n",
       "         'Mayhem Pictures': 4,\n",
       "         'Monkey Dance Productions': 1,\n",
       "         'South Side Amusement Company': 1,\n",
       "         'Clifford Werber Productions': 2,\n",
       "         'SW7D Productions': 1,\n",
       "         'Ciné@': 2,\n",
       "         'Hopscotch Features': 3,\n",
       "         'Delphi V Productions': 6,\n",
       "         'IFP Westcoast Erste': 1,\n",
       "         'CN Film': 1,\n",
       "         'Pacific Film and Television Commission': 3,\n",
       "         'WWE Studios': 11,\n",
       "         'Stampede Entertainment': 1,\n",
       "         'Centropolis Entertainment': 11,\n",
       "         \"Mel's Cite du Cinema\": 12,\n",
       "         'American Film Company': 2,\n",
       "         'Melnitsa Animation Studio': 2,\n",
       "         'Platinum Dunes': 17,\n",
       "         'Poison L.P.': 1,\n",
       "         'icon': 2,\n",
       "         'Andell Entertainment': 2,\n",
       "         'Brat Na Pont Productions': 1,\n",
       "         'Mutual Film Company': 13,\n",
       "         'Magnolia Pictures': 7,\n",
       "         'Madhouse Entertainment': 3,\n",
       "         'Boy in the Box': 1,\n",
       "         '2 Bridges Productions': 1,\n",
       "         'A2 Entertainment Group': 1,\n",
       "         'Ivory Way Productions': 1,\n",
       "         'Scott Free Productions': 38,\n",
       "         'Cinelou Films': 2,\n",
       "         'Fono Roma': 1,\n",
       "         'Les Films Corona': 1,\n",
       "         'The Criterion Collection': 13,\n",
       "         'Vinyl Films': 5,\n",
       "         'KMP Film Invest': 2,\n",
       "         'Voltage Pictures': 6,\n",
       "         'Sobini Films': 2,\n",
       "         'Wallis-Hazen': 1,\n",
       "         'Sofica Manon': 1,\n",
       "         'Easy Company': 1,\n",
       "         'A Plus Image 2': 1,\n",
       "         'Linson Entertainment': 4,\n",
       "         'Tribeca Productions': 16,\n",
       "         'Truth and Soul Pictures Inc': 1,\n",
       "         'Protozoa Pictures': 8,\n",
       "         'Harvest Filmworks': 1,\n",
       "         'Plantain Films': 1,\n",
       "         'Fox International Productions': 2,\n",
       "         'Silvatar Media': 1,\n",
       "         'Horizon Pictures': 1,\n",
       "         'Pariah Entertainment Group': 4,\n",
       "         'Original Pictures': 3,\n",
       "         'Reliance Entertainment': 17,\n",
       "         'Pariah': 5,\n",
       "         'Lewis Gilbert Productions': 1,\n",
       "         'Modern VideoFilm': 1,\n",
       "         'Renaissance Pictures': 4,\n",
       "         'Castel Film Studio': 1,\n",
       "         'Riviera Films': 1,\n",
       "         'Sforzando Productions': 1,\n",
       "         'Artisan Entertainment': 12,\n",
       "         'Madras Talkies': 5,\n",
       "         'DC Entertainment': 16,\n",
       "         'Cruel & Unusual Films': 2,\n",
       "         'TENCENT PICTURES': 1,\n",
       "         'Wanda Pictures': 2,\n",
       "         'Ghoulardi Film Company': 6,\n",
       "         'Industry Entertainment': 6,\n",
       "         'PCH Films': 1,\n",
       "         'Silverwood Films': 4,\n",
       "         'Electric City Entertainment': 5,\n",
       "         'Tim Burton Productions': 16,\n",
       "         'Motion Picture Associates': 1,\n",
       "         'Aascar Films': 2,\n",
       "         'Carnaby International': 1,\n",
       "         'Molinare Studios': 1,\n",
       "         'Eigerwand Pictures': 1,\n",
       "         'DiNovi Pictures': 12,\n",
       "         'Blossom Films': 2,\n",
       "         'Internationale Filmproduktion': 1,\n",
       "         'Laura Bickford Productions': 1,\n",
       "         'Medienproduktion Poseidon Filmgesellschaft': 1,\n",
       "         'On The Corner Films': 2,\n",
       "         'Nemperor': 1,\n",
       "         'Lorton Entertainment': 1,\n",
       "         'Mint Pictures': 1,\n",
       "         'ABC Motion Pictures': 2,\n",
       "         'Troublemaker Studios': 8,\n",
       "         'On My Own': 1,\n",
       "         'Don Simpson/Jerry Bruckheimer Films': 8,\n",
       "         'Lucasfilm': 22,\n",
       "         'Snoot Entertainment': 5,\n",
       "         'Moving Picture Company (MPC)': 13,\n",
       "         'Berlanti Productions': 1,\n",
       "         'RatPac-Dune Entertainment': 17,\n",
       "         'Gramercy Pictures': 6,\n",
       "         'Alphaville Productions': 2,\n",
       "         '360 Pictures': 3,\n",
       "         'Dribble Productions': 1,\n",
       "         'Platinum Equity': 1,\n",
       "         'Laurel Productions': 2,\n",
       "         'Darkside Movie': 1,\n",
       "         'Allspark Pictures': 2,\n",
       "         'LStar Capital': 16,\n",
       "         'Ollin Studio': 1,\n",
       "         'Mad Dog Productions': 1,\n",
       "         'DENTSU Music And Entertainment': 2,\n",
       "         'Tezuka Production Company Ltd.': 2,\n",
       "         'King Record Co.': 1,\n",
       "         'Kadokawa Shoten Publishing Co.': 5,\n",
       "         'Sony Pictures Television': 1,\n",
       "         'Studio 4°C': 1,\n",
       "         'Imagica Corp.': 1,\n",
       "         'Metropolis Project': 1,\n",
       "         'The Caddo Company': 2,\n",
       "         ...})"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "company_counters = collections.Counter()\n",
    "for company in data['company']:\n",
    "    for c in company:\n",
    "        company_counters[c]+=1\n",
    "company_counters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 选取前30的公司作one-hot\n",
    "company30 = sorted(list(company_counters.items()), key=lambda x: x[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Warner Bros.', 491),\n",
       " ('Universal Pictures', 463),\n",
       " ('Paramount Pictures', 393),\n",
       " ('Twentieth Century Fox Film Corporation', 341),\n",
       " ('Columbia Pictures', 236),\n",
       " ('Metro-Goldwyn-Mayer (MGM)', 207),\n",
       " ('New Line Cinema', 198),\n",
       " ('Touchstone Pictures', 158),\n",
       " ('Walt Disney Pictures', 147),\n",
       " ('Columbia Pictures Corporation', 140),\n",
       " ('Canal+', 130),\n",
       " ('TriStar Pictures', 121),\n",
       " ('Relativity Media', 115),\n",
       " ('United Artists', 105),\n",
       " ('Miramax Films', 104),\n",
       " ('Village Roadshow Pictures', 89),\n",
       " ('Regency Enterprises', 81),\n",
       " ('DreamWorks SKG', 78),\n",
       " ('Fox Searchlight Pictures', 69),\n",
       " ('Amblin Entertainment', 68),\n",
       " ('Lionsgate', 68),\n",
       " ('StudioCanal', 65),\n",
       " ('Working Title Films', 63),\n",
       " ('Dune Entertainment', 62),\n",
       " ('Summit Entertainment', 61),\n",
       " ('Dimension Films', 60),\n",
       " ('BBC Films', 56),\n",
       " ('Orion Pictures', 56),\n",
       " ('Hollywood Pictures', 55),\n",
       " ('Fox 2000 Pictures', 52)]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "company30[:30]# 大公司出品的电影占了多数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in company30[:30]:\n",
    "    data[col[0]] = data['company'].map(lambda x: 1 if col[0] in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7398, 142)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'United States of America': 5617,\n",
       "         'India': 220,\n",
       "         'South Korea': 58,\n",
       "         'Canada': 323,\n",
       "         'Serbia': 5,\n",
       "         'United Kingdom': 917,\n",
       "         'Austria': 20,\n",
       "         'Germany': 411,\n",
       "         'France': 570,\n",
       "         'New Zealand': 37,\n",
       "         'Japan': 157,\n",
       "         'Ireland': 62,\n",
       "         'Italy': 160,\n",
       "         'Israel': 16,\n",
       "         'Belgium': 64,\n",
       "         'Czech Republic': 30,\n",
       "         'China': 99,\n",
       "         'Brazil': 23,\n",
       "         'Argentina': 15,\n",
       "         'Chile': 10,\n",
       "         'Peru': 4,\n",
       "         'Hong Kong': 96,\n",
       "         'Russia': 132,\n",
       "         'Spain': 139,\n",
       "         'Turkey': 13,\n",
       "         'Australia': 148,\n",
       "         'Sweden': 50,\n",
       "         'Luxembourg': 19,\n",
       "         'South Africa': 24,\n",
       "         'Switzerland': 26,\n",
       "         'Iran': 6,\n",
       "         'Morocco': 6,\n",
       "         'Netherlands': 43,\n",
       "         'Philippines': 7,\n",
       "         'Iceland': 8,\n",
       "         'Denmark': 40,\n",
       "         'Taiwan': 11,\n",
       "         'Mongolia': 1,\n",
       "         'Hungary': 17,\n",
       "         'Mexico': 44,\n",
       "         'Romania': 18,\n",
       "         'Greece': 9,\n",
       "         'United Arab Emirates': 16,\n",
       "         'Puerto Rico': 3,\n",
       "         'Finland': 18,\n",
       "         'Cambodia': 2,\n",
       "         'Norway': 23,\n",
       "         'Poland': 17,\n",
       "         'Malta': 5,\n",
       "         'Namibia': 1,\n",
       "         'Bosnia and Herzegovina': 1,\n",
       "         'Serbia and Montenegro': 1,\n",
       "         'Pakistan': 3,\n",
       "         'Algeria': 3,\n",
       "         'Ethiopia': 1,\n",
       "         'Qatar': 3,\n",
       "         'Tunisia': 4,\n",
       "         'Portugal': 4,\n",
       "         'Bulgaria': 7,\n",
       "         'Slovenia': 1,\n",
       "         'Ukraine': 7,\n",
       "         'Saudi Arabia': 1,\n",
       "         'Jordan': 1,\n",
       "         'Croatia': 3,\n",
       "         'Singapore': 3,\n",
       "         'Cyprus': 1,\n",
       "         'Uruguay': 1,\n",
       "         'Paraguay': 1,\n",
       "         'Mauritania': 2,\n",
       "         'Costa Rica': 1,\n",
       "         'Bahamas': 3,\n",
       "         'Burkina Faso': 3,\n",
       "         'Ghana': 1,\n",
       "         'Indonesia': 5,\n",
       "         'Botswana': 1,\n",
       "         'Georgia': 1,\n",
       "         'Thailand': 13,\n",
       "         'Afghanistan': 1,\n",
       "         'Colombia': 1,\n",
       "         'Venezuela': 2,\n",
       "         'Dominican Republic': 1,\n",
       "         'Mali': 1,\n",
       "         \"Cote D'Ivoire\": 1,\n",
       "         'Monaco': 2,\n",
       "         'Cameroon': 1,\n",
       "         'Senegal': 1,\n",
       "         'Ecuador': 2,\n",
       "         'Sri Lanka': 1,\n",
       "         'Angola': 1,\n",
       "         'Kazakhstan': 1,\n",
       "         'Liechtenstein': 1,\n",
       "         'Lithuania': 1,\n",
       "         'Palestinian Territory': 1,\n",
       "         'Congo': 1,\n",
       "         'Bolivia': 1,\n",
       "         'Macedonia': 1,\n",
       "         'Malaysia': 1,\n",
       "         'Jamaica': 1})"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_counters = collections.Counter()\n",
    "for country in data['country']:\n",
    "    for c in country:\n",
    "        country_counters[c]+=1\n",
    "country_counters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('United States of America', 5617),\n",
       " ('United Kingdom', 917),\n",
       " ('France', 570),\n",
       " ('Germany', 411),\n",
       " ('Canada', 323),\n",
       " ('India', 220),\n",
       " ('Italy', 160),\n",
       " ('Japan', 157),\n",
       " ('Australia', 148),\n",
       " ('Spain', 139),\n",
       " ('Russia', 132),\n",
       " ('China', 99),\n",
       " ('Hong Kong', 96),\n",
       " ('Belgium', 64),\n",
       " ('Ireland', 62),\n",
       " ('South Korea', 58),\n",
       " ('Sweden', 50),\n",
       " ('Mexico', 44),\n",
       " ('Netherlands', 43),\n",
       " ('Denmark', 40),\n",
       " ('New Zealand', 37),\n",
       " ('Czech Republic', 30),\n",
       " ('Switzerland', 26),\n",
       " ('South Africa', 24),\n",
       " ('Brazil', 23),\n",
       " ('Norway', 23),\n",
       " ('Austria', 20),\n",
       " ('Luxembourg', 19),\n",
       " ('Romania', 18),\n",
       " ('Finland', 18),\n",
       " ('Hungary', 17),\n",
       " ('Poland', 17),\n",
       " ('Israel', 16),\n",
       " ('United Arab Emirates', 16),\n",
       " ('Argentina', 15),\n",
       " ('Turkey', 13),\n",
       " ('Thailand', 13),\n",
       " ('Taiwan', 11),\n",
       " ('Chile', 10),\n",
       " ('Greece', 9),\n",
       " ('Iceland', 8),\n",
       " ('Philippines', 7),\n",
       " ('Bulgaria', 7),\n",
       " ('Ukraine', 7),\n",
       " ('Iran', 6),\n",
       " ('Morocco', 6),\n",
       " ('Serbia', 5),\n",
       " ('Malta', 5),\n",
       " ('Indonesia', 5),\n",
       " ('Peru', 4),\n",
       " ('Tunisia', 4),\n",
       " ('Portugal', 4),\n",
       " ('Puerto Rico', 3),\n",
       " ('Pakistan', 3),\n",
       " ('Algeria', 3),\n",
       " ('Qatar', 3),\n",
       " ('Croatia', 3),\n",
       " ('Singapore', 3),\n",
       " ('Bahamas', 3),\n",
       " ('Burkina Faso', 3),\n",
       " ('Cambodia', 2),\n",
       " ('Mauritania', 2),\n",
       " ('Venezuela', 2),\n",
       " ('Monaco', 2),\n",
       " ('Ecuador', 2),\n",
       " ('Mongolia', 1),\n",
       " ('Namibia', 1),\n",
       " ('Bosnia and Herzegovina', 1),\n",
       " ('Serbia and Montenegro', 1),\n",
       " ('Ethiopia', 1),\n",
       " ('Slovenia', 1),\n",
       " ('Saudi Arabia', 1),\n",
       " ('Jordan', 1),\n",
       " ('Cyprus', 1),\n",
       " ('Uruguay', 1),\n",
       " ('Paraguay', 1),\n",
       " ('Costa Rica', 1),\n",
       " ('Ghana', 1),\n",
       " ('Botswana', 1),\n",
       " ('Georgia', 1),\n",
       " ('Afghanistan', 1),\n",
       " ('Colombia', 1),\n",
       " ('Dominican Republic', 1),\n",
       " ('Mali', 1),\n",
       " (\"Cote D'Ivoire\", 1),\n",
       " ('Cameroon', 1),\n",
       " ('Senegal', 1),\n",
       " ('Sri Lanka', 1),\n",
       " ('Angola', 1),\n",
       " ('Kazakhstan', 1),\n",
       " ('Liechtenstein', 1),\n",
       " ('Lithuania', 1),\n",
       " ('Palestinian Territory', 1),\n",
       " ('Congo', 1),\n",
       " ('Bolivia', 1),\n",
       " ('Macedonia', 1),\n",
       " ('Malaysia', 1),\n",
       " ('Jamaica', 1)]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 选择前20的国家做one-hot\n",
    "countrys = sorted(list(country_counters.items()), key=lambda x: x[1], reverse=True)\n",
    "countrys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('United States of America', 5617),\n",
       " ('United Kingdom', 917),\n",
       " ('France', 570),\n",
       " ('Germany', 411),\n",
       " ('Canada', 323),\n",
       " ('India', 220),\n",
       " ('Italy', 160),\n",
       " ('Japan', 157),\n",
       " ('Australia', 148),\n",
       " ('Spain', 139),\n",
       " ('Russia', 132),\n",
       " ('China', 99),\n",
       " ('Hong Kong', 96),\n",
       " ('Belgium', 64),\n",
       " ('Ireland', 62),\n",
       " ('South Korea', 58),\n",
       " ('Sweden', 50),\n",
       " ('Mexico', 44),\n",
       " ('Netherlands', 43),\n",
       " ('Denmark', 40)]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countrys[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in countrys[:20]:\n",
    "    data[col[0]] = data['country'].map(lambda x: 1 if col[0] in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['country_num'] = data['country'].map(lambda x: len(x))\n",
    "data['company_num'] = data['company'].map(lambda x: len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "revenue                 4398\n",
       "Keywords                 669\n",
       "production_companies     414\n",
       "production_countries     157\n",
       "crew                      38\n",
       "cast                      26\n",
       "dtype: int64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_count(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Keywords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 增加keywords数目列\n",
    "# 统计关键词个数，选择出现次数较多的构造one_hot\n",
    "def keywords_abstract(x):\n",
    "    temp = eval(x) if isinstance(x, str) else []\n",
    "    ans = []\n",
    "    for item in temp:\n",
    "        ans.append(item['name'])\n",
    "    return ans\n",
    "data['keyword'] = data['Keywords'].map(keywords_abstract)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['keyword_num'] = data['keyword'].map(lambda x: len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'time travel': 58,\n",
       "         'sequel': 158,\n",
       "         'hot tub': 3,\n",
       "         'duringcreditsstinger': 350,\n",
       "         'coronation': 2,\n",
       "         'duty': 4,\n",
       "         'marriage': 73,\n",
       "         'falling in love': 27,\n",
       "         'jazz': 17,\n",
       "         'obsession': 50,\n",
       "         'conservatory': 1,\n",
       "         'music teacher': 4,\n",
       "         'new york city': 85,\n",
       "         'violence': 245,\n",
       "         'montage': 1,\n",
       "         'drummer': 3,\n",
       "         'public humiliation': 4,\n",
       "         'jazz band': 2,\n",
       "         'young adult': 21,\n",
       "         'music school': 2,\n",
       "         'mystery': 13,\n",
       "         'bollywood': 26,\n",
       "         'police corruption': 10,\n",
       "         'crime': 33,\n",
       "         'india': 25,\n",
       "         'missing husband': 2,\n",
       "         'nerve gas': 2,\n",
       "         'journalism': 19,\n",
       "         'translation': 2,\n",
       "         'television': 14,\n",
       "         'manipulation of the media': 4,\n",
       "         'iraq': 12,\n",
       "         'reporter': 32,\n",
       "         'woman director': 457,\n",
       "         'island': 53,\n",
       "         'pirate gang': 4,\n",
       "         'puppet': 19,\n",
       "         'treasure hunt': 25,\n",
       "         'mockumentary': 17,\n",
       "         'folk singer': 2,\n",
       "         'underdog': 23,\n",
       "         'philadelphia': 7,\n",
       "         'transporter': 14,\n",
       "         'italo-american': 8,\n",
       "         'fight': 69,\n",
       "         \"love of one's life\": 61,\n",
       "         'publicity': 5,\n",
       "         'boxer': 20,\n",
       "         'independence': 9,\n",
       "         'boxing match': 12,\n",
       "         'training': 32,\n",
       "         'lovers': 25,\n",
       "         'surprise': 4,\n",
       "         'world champion': 7,\n",
       "         'amateur': 1,\n",
       "         'victory': 7,\n",
       "         'nerd': 16,\n",
       "         'vacation': 22,\n",
       "         'farce': 4,\n",
       "         'jock': 2,\n",
       "         'frame up': 3,\n",
       "         'defector': 1,\n",
       "         'male nudity': 61,\n",
       "         'female nudity': 130,\n",
       "         'adultery': 54,\n",
       "         'parent child relationship': 37,\n",
       "         'midlife crisis': 19,\n",
       "         'coming out': 12,\n",
       "         'first time': 9,\n",
       "         'camcorder': 6,\n",
       "         'virgin': 19,\n",
       "         'nudity': 153,\n",
       "         'film maker': 5,\n",
       "         'estate agent': 3,\n",
       "         'satire': 21,\n",
       "         'loneliness': 21,\n",
       "         'dark comedy': 49,\n",
       "         'suburbia': 6,\n",
       "         'coming of age': 45,\n",
       "         'marijuana': 38,\n",
       "         'exercise': 4,\n",
       "         'bittersweet': 6,\n",
       "         'affair': 7,\n",
       "         'baseball bat': 6,\n",
       "         'widow': 24,\n",
       "         'recording contract': 5,\n",
       "         'recording studio': 3,\n",
       "         'russian mafia': 4,\n",
       "         'music business': 3,\n",
       "         'night club': 7,\n",
       "         'pawnshop': 1,\n",
       "         'self-fulfilling prophecy': 3,\n",
       "         'washington d.c.': 21,\n",
       "         'evidence': 9,\n",
       "         'future': 59,\n",
       "         'hologram': 6,\n",
       "         'dystopia': 166,\n",
       "         'murder': 305,\n",
       "         'neo-noir': 40,\n",
       "         'future noir': 1,\n",
       "         'skinhead': 4,\n",
       "         'serbia': 1,\n",
       "         'hostage': 48,\n",
       "         'menace': 7,\n",
       "         'hitman': 57,\n",
       "         'airplane': 73,\n",
       "         'biography': 176,\n",
       "         'charles dickens': 5,\n",
       "         'competition': 40,\n",
       "         'snowboarding': 4,\n",
       "         'birthday party': 5,\n",
       "         'chalet': 1,\n",
       "         'snowboarding competition': 1,\n",
       "         'skiing': 11,\n",
       "         'engagement party': 2,\n",
       "         'martial arts': 89,\n",
       "         'war on drugs': 9,\n",
       "         'kidnapping': 95,\n",
       "         'bodyguard': 14,\n",
       "         'baby-snatching': 10,\n",
       "         'sabotage': 14,\n",
       "         'deep space explorer': 2,\n",
       "         'lake': 16,\n",
       "         'summer camp': 9,\n",
       "         'serial killer': 84,\n",
       "         'slasher': 43,\n",
       "         'summer': 27,\n",
       "         'jason voorhees': 1,\n",
       "         'one by one': 1,\n",
       "         'friday the thirteenth': 1,\n",
       "         'webcam': 4,\n",
       "         'anthology': 12,\n",
       "         'vhs': 2,\n",
       "         'burglary': 8,\n",
       "         'split screen': 3,\n",
       "         'found footage': 36,\n",
       "         'mumblecore': 10,\n",
       "         'ghost child': 3,\n",
       "         'boxer shorts': 3,\n",
       "         'vcr': 1,\n",
       "         'halloween mask': 1,\n",
       "         'mumblegore': 19,\n",
       "         'horror anthology': 5,\n",
       "         'new zealand': 10,\n",
       "         'sheep': 5,\n",
       "         'animal horror': 20,\n",
       "         'dialogue': 6,\n",
       "         'confidence': 11,\n",
       "         'invention': 7,\n",
       "         'independent film': 384,\n",
       "         'male female relationship': 26,\n",
       "         'best friend': 58,\n",
       "         'toy': 12,\n",
       "         'transformation': 25,\n",
       "         'based on toy': 5,\n",
       "         'transformers': 6,\n",
       "         'robots': 1,\n",
       "         'intergalactic war': 1,\n",
       "         'new york': 123,\n",
       "         'custody battle': 6,\n",
       "         'suspense': 159,\n",
       "         'lawyer': 61,\n",
       "         'male friendship': 22,\n",
       "         'masseuse': 4,\n",
       "         'friendship': 168,\n",
       "         'aristocrat': 5,\n",
       "         'interracial friendship': 2,\n",
       "         'unlikely friendship': 12,\n",
       "         'paris': 81,\n",
       "         'upper class': 16,\n",
       "         'puberty': 8,\n",
       "         'anonymous letter': 5,\n",
       "         'suppressed past': 7,\n",
       "         'massacre on french algerians 1961': 1,\n",
       "         'moderator': 4,\n",
       "         'lie': 26,\n",
       "         'algerian': 4,\n",
       "         'videoband': 5,\n",
       "         'intellectual': 3,\n",
       "         'conscience': 3,\n",
       "         'witch': 54,\n",
       "         'uprising': 6,\n",
       "         'witch hunter': 3,\n",
       "         'ambassador': 9,\n",
       "         'broken trachea': 1,\n",
       "         'sex': 186,\n",
       "         'waitress': 27,\n",
       "         'sister sister relationship': 39,\n",
       "         'infidelity': 59,\n",
       "         'seduction': 34,\n",
       "         'brother': 19,\n",
       "         'girlfriend': 16,\n",
       "         'engagement': 8,\n",
       "         'love': 190,\n",
       "         'relationship': 52,\n",
       "         'customer': 1,\n",
       "         'river': 25,\n",
       "         'general': 13,\n",
       "         'research': 10,\n",
       "         'army': 36,\n",
       "         'serum': 3,\n",
       "         'monkey': 21,\n",
       "         'epidemic': 8,\n",
       "         'medical research': 4,\n",
       "         'mexico': 24,\n",
       "         'prisoners of war': 15,\n",
       "         'apache': 4,\n",
       "         'raid': 4,\n",
       "         'confederate': 2,\n",
       "         'cocaine': 18,\n",
       "         'drug dealer': 46,\n",
       "         'prison': 115,\n",
       "         'rebel': 18,\n",
       "         'loss of mother': 22,\n",
       "         'harassment': 8,\n",
       "         'imprisonment': 6,\n",
       "         'dying and death': 94,\n",
       "         'escape': 84,\n",
       "         'barbed wire': 4,\n",
       "         'punched in the face': 4,\n",
       "         'eggs': 2,\n",
       "         'death': 74,\n",
       "         'gay': 62,\n",
       "         'flush': 3,\n",
       "         'yoga': 3,\n",
       "         'single': 27,\n",
       "         'los angeles': 127,\n",
       "         'single father': 14,\n",
       "         'co-parenting': 2,\n",
       "         'married couple': 22,\n",
       "         'moving out': 2,\n",
       "         'moving': 5,\n",
       "         'apartment': 20,\n",
       "         'based on novel': 312,\n",
       "         'callboy': 4,\n",
       "         'wedding': 89,\n",
       "         'escort': 3,\n",
       "         'fake boyfriend': 2,\n",
       "         'suicide': 82,\n",
       "         'hotel': 57,\n",
       "         'dream': 61,\n",
       "         'psychology': 13,\n",
       "         'loss': 12,\n",
       "         'cop': 19,\n",
       "         'microphone': 4,\n",
       "         'photograph': 11,\n",
       "         'death of lover': 3,\n",
       "         'mammoth': 4,\n",
       "         'sloth': 2,\n",
       "         'ice age': 4,\n",
       "         'barrier ice': 2,\n",
       "         'ice melting': 2,\n",
       "         'iceberg': 3,\n",
       "         'flooding': 4,\n",
       "         'adventure': 21,\n",
       "         'deluge': 1,\n",
       "         'saber-toothed tiger': 3,\n",
       "         'bounty hunter': 16,\n",
       "         'wyoming': 5,\n",
       "         'mountain': 28,\n",
       "         'narration': 24,\n",
       "         'hangman': 2,\n",
       "         'stagecoach': 7,\n",
       "         'blizzard': 8,\n",
       "         'post civil war': 2,\n",
       "         'naivety': 7,\n",
       "         'oddball': 2,\n",
       "         'daughter': 84,\n",
       "         'misfit': 11,\n",
       "         'dog': 64,\n",
       "         'divorcee': 4,\n",
       "         'based on graphic novel': 11,\n",
       "         'middle aged man': 2,\n",
       "         'based on tv series': 32,\n",
       "         'exploding planet': 1,\n",
       "         'mountain cabin': 1,\n",
       "         'solar system': 3,\n",
       "         'supermarket': 13,\n",
       "         'truck': 17,\n",
       "         'urban': 3,\n",
       "         'lesbian': 27,\n",
       "         'working class': 6,\n",
       "         'cell phone': 14,\n",
       "         'happiness': 2,\n",
       "         'truck driver': 5,\n",
       "         'tarot': 1,\n",
       "         'slapstick': 10,\n",
       "         'ensemble cast': 14,\n",
       "         'automobile racing': 11,\n",
       "         'cross country race': 2,\n",
       "         'turn of the century': 2,\n",
       "         'kids and family': 12,\n",
       "         'aviation': 4,\n",
       "         'castaway': 1,\n",
       "         'pacific island': 5,\n",
       "         'kids': 11,\n",
       "         'from backpacks to strollers': 1,\n",
       "         'christianity': 15,\n",
       "         'jesus christ': 11,\n",
       "         'apostle': 2,\n",
       "         'crucifixion': 8,\n",
       "         'jerusalem': 5,\n",
       "         'ancient rome': 13,\n",
       "         'faith': 40,\n",
       "         'resurrection': 10,\n",
       "         'dead body': 10,\n",
       "         'judaism': 4,\n",
       "         'religious': 5,\n",
       "         'tomb': 5,\n",
       "         'college': 54,\n",
       "         'thanksgiving': 13,\n",
       "         'farm': 17,\n",
       "         'liar': 13,\n",
       "         'pancreas': 1,\n",
       "         'thong': 1,\n",
       "         'city boy': 1,\n",
       "         'chess': 6,\n",
       "         'bicycle': 9,\n",
       "         'double life': 10,\n",
       "         'dc comics': 30,\n",
       "         'dual identity': 14,\n",
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       "         'human vs nature': 4,\n",
       "         'living together': 8,\n",
       "         'rural setting': 28,\n",
       "         'social commentary': 5,\n",
       "         'nature': 13,\n",
       "         'environment': 3,\n",
       "         'lost civilization': 1,\n",
       "         'epilepsy': 6,\n",
       "         'possession': 18,\n",
       "         'teenage girl': 25,\n",
       "         'spirit': 10,\n",
       "         'umbrella': 1,\n",
       "         'cross': 3,\n",
       "         'prosecutor': 3,\n",
       "         'catholicism': 15,\n",
       "         'negligent homicide': 1,\n",
       "         'archdiocese': 1,\n",
       "         'agnostic': 1,\n",
       "         'malnutrition': 1,\n",
       "         'burning': 1,\n",
       "         'psychotic epileptic disorder': 1,\n",
       "         'diamant': 6,\n",
       "         \"c√¥te d'azur\": 2,\n",
       "         'inspector': 7,\n",
       "         'panther': 3,\n",
       "         'explosive': 11,\n",
       "         'boomerang': 1,\n",
       "         'pilot': 29,\n",
       "         'chase': 54,\n",
       "         'deal': 4,\n",
       "         'survivor': 19,\n",
       "         'community': 6,\n",
       "         'ex-cop': 16,\n",
       "         'oil': 7,\n",
       "         'wasteland': 2,\n",
       "         'gang rape': 2,\n",
       "         'desolate': 1,\n",
       "         'oil refinery': 1,\n",
       "         'disfigurement mask': 2,\n",
       "         'music box': 2,\n",
       "         'oil tanker': 1,\n",
       "         'wanderer': 1,\n",
       "         'ozploitation': 5,\n",
       "         'police chief': 8,\n",
       "         'spying': 4,\n",
       "         'police academy': 4,\n",
       "         'commandant': 2,\n",
       "         'con man': 17,\n",
       "         'fraud': 13,\n",
       "         'gambling debts': 7,\n",
       "         'noir': 3,\n",
       "         'private eye': 4,\n",
       "         'lew harper': 1,\n",
       "         'marooned': 2,\n",
       "         'deserted island': 6,\n",
       "         'tropical island': 6,\n",
       "         'australian outback': 6,\n",
       "         'street gang': 26,\n",
       "         'aftercreditsstinger': 183,\n",
       "         'starships': 2,\n",
       "         'star': 9,\n",
       "         'supernova': 2,\n",
       "         'blast': 6,\n",
       "         'vietnam veteran': 16,\n",
       "         'falsely accused': 15,\n",
       "         'sheriff': 45,\n",
       "         'guerrilla': 5,\n",
       "         'submachine gun': 9,\n",
       "         'gun': 44,\n",
       "         'destroy': 10,\n",
       "         'self-defense': 8,\n",
       "         'vietnam': 16,\n",
       "         ...})"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "key_counter = collections.Counter()\n",
    "for keywords in data['keyword']:\n",
    "    for word in keywords:\n",
    "        key_counter[word]+=1\n",
    "key_counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "keys_sort = sorted(list(key_counter.items()), key=lambda x:x[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('woman director', 457),\n",
       " ('independent film', 384),\n",
       " ('duringcreditsstinger', 350),\n",
       " ('based on novel', 312),\n",
       " ('murder', 305),\n",
       " ('violence', 245),\n",
       " ('love', 190),\n",
       " ('revenge', 188),\n",
       " ('sex', 186),\n",
       " ('aftercreditsstinger', 183),\n",
       " ('biography', 176),\n",
       " ('sport', 175),\n",
       " ('friendship', 168),\n",
       " ('dystopia', 166),\n",
       " ('police', 160),\n",
       " ('suspense', 159),\n",
       " ('sequel', 158),\n",
       " ('nudity', 153),\n",
       " ('musical', 147),\n",
       " ('teenager', 145),\n",
       " ('female nudity', 130),\n",
       " ('drug', 130),\n",
       " ('los angeles', 127),\n",
       " ('new york', 123),\n",
       " ('prison', 115),\n",
       " ('3d', 113),\n",
       " ('high school', 111),\n",
       " ('family', 111),\n",
       " ('alien', 100),\n",
       " ('world war ii', 98),\n",
       " ('kidnapping', 95),\n",
       " ('dying and death', 94),\n",
       " ('london england', 93),\n",
       " ('father son relationship', 92),\n",
       " ('martial arts', 89),\n",
       " ('wedding', 89),\n",
       " ('based on true story', 88),\n",
       " ('remake', 87),\n",
       " ('new york city', 85),\n",
       " ('serial killer', 84),\n",
       " ('escape', 84),\n",
       " ('daughter', 84),\n",
       " ('rape', 84),\n",
       " ('investigation', 84),\n",
       " ('superhero', 83),\n",
       " ('suicide', 82),\n",
       " ('paris', 81),\n",
       " ('magic', 81),\n",
       " ('robbery', 80),\n",
       " ('party', 79),\n",
       " ('based on comic', 79),\n",
       " ('detective', 78),\n",
       " ('hospital', 77),\n",
       " ('death', 74),\n",
       " ('corruption', 74),\n",
       " ('friends', 74),\n",
       " ('brother brother relationship', 74),\n",
       " ('marriage', 73),\n",
       " ('airplane', 73),\n",
       " ('prostitute', 73),\n",
       " ('blood', 71),\n",
       " ('monster', 71),\n",
       " ('fight', 69),\n",
       " ('jealousy', 69),\n",
       " ('money', 68),\n",
       " ('wife husband relationship', 68),\n",
       " ('war', 66),\n",
       " ('vampire', 65),\n",
       " ('dog', 64),\n",
       " ('gay', 62),\n",
       " ('small town', 62),\n",
       " ('journalist', 62),\n",
       " ('explosion', 62),\n",
       " (\"love of one's life\", 61),\n",
       " ('male nudity', 61),\n",
       " ('lawyer', 61),\n",
       " ('dream', 61),\n",
       " ('gangster', 60),\n",
       " ('cia', 60),\n",
       " ('teacher', 60),\n",
       " ('future', 59),\n",
       " ('infidelity', 59),\n",
       " ('gore', 59),\n",
       " ('rescue', 59),\n",
       " ('time travel', 58),\n",
       " ('best friend', 58),\n",
       " ('secret', 58),\n",
       " ('music', 58),\n",
       " ('flashback', 58),\n",
       " ('hitman', 57),\n",
       " ('hotel', 57),\n",
       " ('shootout', 57),\n",
       " ('psychopath', 56),\n",
       " ('extramarital affair', 56),\n",
       " ('assassin', 56),\n",
       " ('romance', 55),\n",
       " ('fbi', 55),\n",
       " ('alcohol', 55),\n",
       " ('scientist', 55),\n",
       " ('adultery', 54),\n",
       " ('witch', 54),\n",
       " ('college', 54),\n",
       " ('gang', 54),\n",
       " ('chase', 54),\n",
       " ('spy', 54),\n",
       " ('island', 53),\n",
       " ('blackmail', 53),\n",
       " ('relationship', 52),\n",
       " ('divorce', 52),\n",
       " ('doctor', 52),\n",
       " ('post-apocalyptic', 52),\n",
       " ('helicopter', 51),\n",
       " ('obsession', 50),\n",
       " ('soldier', 50),\n",
       " ('undercover', 50),\n",
       " ('christmas', 50),\n",
       " ('dark comedy', 49),\n",
       " ('brother sister relationship', 49),\n",
       " ('holiday', 49),\n",
       " ('1970s', 49),\n",
       " ('hostage', 48),\n",
       " ('torture', 48),\n",
       " ('survival', 48),\n",
       " ('mother daughter relationship', 48),\n",
       " ('romantic comedy', 48),\n",
       " ('criminal', 48),\n",
       " ('student', 48),\n",
       " ('zombie', 48),\n",
       " ('animation', 47),\n",
       " ('ghost', 47),\n",
       " ('based on play or musical', 47),\n",
       " ('drug dealer', 46),\n",
       " ('conspiracy', 46),\n",
       " ('fbi agent', 46),\n",
       " ('coming of age', 45),\n",
       " ('supernatural', 45),\n",
       " ('car crash', 45),\n",
       " ('new love', 45),\n",
       " ('sheriff', 45),\n",
       " ('england', 45),\n",
       " ('neighbor', 44),\n",
       " ('gun', 44),\n",
       " ('demon', 44),\n",
       " ('writer', 44),\n",
       " ('slasher', 43),\n",
       " ('terrorist', 43),\n",
       " ('road trip', 43),\n",
       " ('nazis', 43),\n",
       " ('baby', 43),\n",
       " ('killer', 43),\n",
       " ('marvel comic', 43),\n",
       " ('hero', 43),\n",
       " ('california', 43),\n",
       " ('robot', 43),\n",
       " ('car chase', 42),\n",
       " ('based on young adult novel', 42),\n",
       " ('rivalry', 42),\n",
       " ('alcoholic', 42),\n",
       " ('motorcycle', 41),\n",
       " ('beach', 41),\n",
       " ('pregnancy', 41),\n",
       " ('assassination', 41),\n",
       " ('family relationships', 41),\n",
       " ('neo-noir', 40),\n",
       " ('competition', 40),\n",
       " ('faith', 40),\n",
       " ('texas', 40),\n",
       " ('secret identity', 40),\n",
       " ('politics', 40),\n",
       " ('funeral', 40),\n",
       " ('sister sister relationship', 39),\n",
       " ('nightmare', 39),\n",
       " ('saving the world', 39),\n",
       " ('love triangle', 39),\n",
       " ('school', 39),\n",
       " ('marijuana', 38),\n",
       " ('ship', 38),\n",
       " ('usa president', 38),\n",
       " ('desert', 38),\n",
       " ('battle', 38),\n",
       " ('parent child relationship', 37),\n",
       " ('orphan', 37),\n",
       " ('san francisco', 37),\n",
       " ('france', 37),\n",
       " ('baseball', 37),\n",
       " ('forest', 37),\n",
       " ('found footage', 36),\n",
       " ('army', 36),\n",
       " ('japan', 36),\n",
       " ('jungle', 36),\n",
       " ('thief', 36),\n",
       " ('comedy', 36),\n",
       " ('homosexuality', 36),\n",
       " ('heist', 36),\n",
       " ('author', 36),\n",
       " ('amnesia', 35),\n",
       " ('australia', 35),\n",
       " ('loss of father', 35),\n",
       " ('betrayal', 35),\n",
       " ('kiss', 35),\n",
       " ('usa', 35),\n",
       " ('seduction', 34),\n",
       " ('restaurant', 34),\n",
       " ('space', 34),\n",
       " ('bomb', 34),\n",
       " ('based on video game', 34),\n",
       " ('motel', 34),\n",
       " ('crime', 33),\n",
       " ('black people', 33),\n",
       " ('church', 33),\n",
       " ('dancing', 33),\n",
       " ('paranoia', 33),\n",
       " ('father daughter relationship', 33),\n",
       " ('princess', 33),\n",
       " ('reporter', 32),\n",
       " ('training', 32),\n",
       " ('based on tv series', 32),\n",
       " ('super powers', 32),\n",
       " ('fire', 32),\n",
       " ('mutant', 32),\n",
       " ('artificial intelligence', 32),\n",
       " ('bar', 32),\n",
       " ('dance', 32),\n",
       " ('government', 32),\n",
       " ('gunfight', 32),\n",
       " ('chicago', 32),\n",
       " ('priest', 32),\n",
       " ('deception', 32),\n",
       " ('racism', 32),\n",
       " ('mafia', 32),\n",
       " ('kung fu', 31),\n",
       " ('historical figure', 31),\n",
       " ('extreme violence', 31),\n",
       " ('space marine', 31),\n",
       " ('military', 31),\n",
       " ('suicide attempt', 31),\n",
       " ('dc comics', 30),\n",
       " ('singer', 30),\n",
       " ('horse', 30),\n",
       " ('insanity', 30),\n",
       " ('travel', 30),\n",
       " ('mission', 30),\n",
       " ('cold war', 30),\n",
       " ('court case', 29),\n",
       " ('space travel', 29),\n",
       " ('hollywood', 29),\n",
       " ('pilot', 29),\n",
       " ('eroticism', 29),\n",
       " ('spoof', 29),\n",
       " ('anime', 29),\n",
       " ('secret agent', 29),\n",
       " ('gambling', 29),\n",
       " ('mountain', 28),\n",
       " ('train', 28),\n",
       " ('adoption', 28),\n",
       " ('rural setting', 28),\n",
       " ('spaceship', 28),\n",
       " ('android', 28),\n",
       " ('female protagonist', 28),\n",
       " ('millionaire', 28),\n",
       " ('terror', 28),\n",
       " ('cheating', 28),\n",
       " ('cat', 28),\n",
       " ('africa', 28),\n",
       " ('imax', 28),\n",
       " ('falling in love', 27),\n",
       " ('summer', 27),\n",
       " ('waitress', 27),\n",
       " ('single', 27),\n",
       " ('lesbian', 27),\n",
       " ('china', 27),\n",
       " ('based on true events', 27),\n",
       " ('castle', 27),\n",
       " ('mother son relationship', 27),\n",
       " ('terrorism', 27),\n",
       " ('weapon', 27),\n",
       " ('children', 27),\n",
       " ('on the run', 27),\n",
       " ('american football', 27),\n",
       " ('police officer', 27),\n",
       " ('werewolf', 27),\n",
       " ('bollywood', 26),\n",
       " ('male female relationship', 26),\n",
       " ('lie', 26),\n",
       " ('lgbt', 26),\n",
       " ('curse', 26),\n",
       " ('court', 26),\n",
       " ('hallucination', 26),\n",
       " ('psychiatrist', 26),\n",
       " ('disaster', 26),\n",
       " ('19th century', 26),\n",
       " ('street gang', 26),\n",
       " ('swimming pool', 26),\n",
       " ('wilderness', 26),\n",
       " ('hoodlum', 26),\n",
       " ('fairy tale', 26),\n",
       " ('queen', 26),\n",
       " ('airport', 26),\n",
       " ('road movie', 26),\n",
       " ('fantasy', 26),\n",
       " ('cult film', 26),\n",
       " ('india', 25),\n",
       " ('treasure hunt', 25),\n",
       " ('lovers', 25),\n",
       " ('transformation', 25),\n",
       " ('river', 25),\n",
       " ('car', 25),\n",
       " ('astronaut', 25),\n",
       " ('individual', 25),\n",
       " ('intelligence', 25),\n",
       " ('sea', 25),\n",
       " ('gold', 25),\n",
       " ('winter', 25),\n",
       " ('snow', 25),\n",
       " ('vigilante', 25),\n",
       " ('teenage girl', 25),\n",
       " ('mobster', 25),\n",
       " ('surrealism', 25),\n",
       " ('hacker', 25),\n",
       " ('computer', 25),\n",
       " ('cyberpunk', 25),\n",
       " ('child abuse', 25),\n",
       " ('film noir', 25),\n",
       " ('incest', 25),\n",
       " ('prince', 25),\n",
       " ('wheelchair', 25),\n",
       " ('alien invasion', 25),\n",
       " ('widow', 24),\n",
       " ('mexico', 24),\n",
       " ('narration', 24),\n",
       " ('evil', 24),\n",
       " ('boat', 24),\n",
       " ('mask', 24),\n",
       " ('child', 24),\n",
       " ('police brutality', 24),\n",
       " ('nurse', 24),\n",
       " ('southern usa', 24),\n",
       " ('sword', 24),\n",
       " ('loss of lover', 24),\n",
       " ('photographer', 24),\n",
       " ('florida', 24),\n",
       " ('sniper', 24),\n",
       " ('forbidden love', 24),\n",
       " ('dinosaur', 24),\n",
       " ('book', 24),\n",
       " ('cancer', 24),\n",
       " ('politician', 24),\n",
       " ('female friendship', 24),\n",
       " ('underdog', 23),\n",
       " ('casino', 23),\n",
       " ('religion', 23),\n",
       " ('becoming an adult', 23),\n",
       " ('village', 23),\n",
       " ('1980s', 23),\n",
       " ('talking animal', 23),\n",
       " ('dysfunctional family', 23),\n",
       " ('world war i', 23),\n",
       " ('bank', 23),\n",
       " ('fugitive', 23),\n",
       " ('strip club', 23),\n",
       " ('experiment', 23),\n",
       " ('career', 23),\n",
       " ('secret love', 23),\n",
       " ('showdown', 23),\n",
       " ('drug addiction', 23),\n",
       " ('marriage crisis', 23),\n",
       " ('1960s', 23),\n",
       " ('father', 23),\n",
       " ('lovesickness', 23),\n",
       " ('space opera', 23),\n",
       " ('boy', 23),\n",
       " ('professor', 23),\n",
       " ('vacation', 22),\n",
       " ('male friendship', 22),\n",
       " ('loss of mother', 22),\n",
       " ('married couple', 22),\n",
       " ('sexuality', 22),\n",
       " ('painting', 22),\n",
       " ('outer space', 22),\n",
       " ('treasure', 22),\n",
       " ('scotland', 22),\n",
       " ('ocean', 22),\n",
       " ('shipwreck', 22),\n",
       " ('cyborg', 22),\n",
       " ('single mother', 22),\n",
       " ('nightclub', 22),\n",
       " ('taxi', 22),\n",
       " ('native american', 22),\n",
       " ('russia', 22),\n",
       " ('slavery', 22),\n",
       " ('halloween', 22),\n",
       " ('apocalypse', 22),\n",
       " ('outlaw', 22),\n",
       " ('young adult', 21),\n",
       " ('satire', 21),\n",
       " ('loneliness', 21),\n",
       " ('washington d.c.', 21),\n",
       " ('monkey', 21),\n",
       " ('adventure', 21),\n",
       " ('italy', 21),\n",
       " ('riddle', 21),\n",
       " ('isolation', 21),\n",
       " ('epic', 21),\n",
       " ('las vegas', 21),\n",
       " ('vietnam war', 21),\n",
       " ('organized crime', 21),\n",
       " ('alcoholism', 21),\n",
       " ('virtual reality', 21),\n",
       " ('age difference', 21),\n",
       " ('youth', 21),\n",
       " ('teen movie', 21),\n",
       " ('egypt', 21),\n",
       " ('mother', 21),\n",
       " ('corpse', 21),\n",
       " ('fistfight', 21),\n",
       " ('mercenary', 21),\n",
       " ('archaeologist', 21),\n",
       " ('disappearance', 21),\n",
       " ('fear', 21),\n",
       " ('submarine', 21),\n",
       " ('theft', 21),\n",
       " ('unsociability', 21),\n",
       " ('boxer', 20),\n",
       " ('animal horror', 20),\n",
       " ('apartment', 20),\n",
       " ('car race', 20),\n",
       " ('prisoner', 20),\n",
       " ('heroin', 20),\n",
       " ('death of a friend', 20),\n",
       " ('sister', 20),\n",
       " ('immortality', 20),\n",
       " ('cowardliness', 20),\n",
       " ('mass murder', 20),\n",
       " ('anthropomorphism', 20),\n",
       " ('good vs evil', 20),\n",
       " ('resistance', 20),\n",
       " ('artist', 20),\n",
       " ('animal', 20),\n",
       " ('decapitation', 20),\n",
       " ('private detective', 20),\n",
       " ('birthday', 20),\n",
       " ('memory', 20),\n",
       " ('depression', 20),\n",
       " ('journalism', 19),\n",
       " ('puppet', 19),\n",
       " ('midlife crisis', 19),\n",
       " ('virgin', 19),\n",
       " ('mumblegore', 19),\n",
       " ('brother', 19),\n",
       " ('cop', 19),\n",
       " ('series of murders', 19),\n",
       " ('rock and roll', 19),\n",
       " ('sadism', 19),\n",
       " ('sword and sorcery', 19),\n",
       " ('subway', 19),\n",
       " ('prophecy', 19),\n",
       " ('stalker', 19),\n",
       " ('survivor', 19),\n",
       " ('ireland', 19),\n",
       " ('sword fight', 19),\n",
       " ('brutality', 19),\n",
       " ('berlin', 19),\n",
       " ('exotic island', 19),\n",
       " ('pirate', 19),\n",
       " ('marriage proposal', 19),\n",
       " ('musician', 19),\n",
       " ('grief', 19),\n",
       " ('chaos', 19),\n",
       " ('therapist', 19),\n",
       " ('dragon', 19),\n",
       " ('tattoo', 19),\n",
       " ('concert', 19),\n",
       " ('flying', 19),\n",
       " ('roommate', 19),\n",
       " ('one-night stand', 19),\n",
       " ('boston', 19),\n",
       " ('judge', 19),\n",
       " ('christian', 19),\n",
       " ('cocaine', 18),\n",
       " ('rebel', 18),\n",
       " ('crime fighter', 18),\n",
       " ('hong kong', 18),\n",
       " ('suspicion of murder', 18),\n",
       " ('coma', 18),\n",
       " ('police operation', 18),\n",
       " ('pistol', 18),\n",
       " ('possession', 18),\n",
       " ('freedom', 18),\n",
       " ('traitor', 18),\n",
       " ('bank robbery', 18),\n",
       " ('single parent', 18),\n",
       " ('bear', 18),\n",
       " ('surveillance', 18),\n",
       " ('christmas party', 18),\n",
       " ('supernatural powers', 18),\n",
       " ('courtroom', 18),\n",
       " ('haunted house', 18),\n",
       " ('celebration', 18),\n",
       " ('radio', 18),\n",
       " ('science', 18),\n",
       " ('knight', 18),\n",
       " ('1940s', 18),\n",
       " ('inventor', 18),\n",
       " ('mission of murder', 18),\n",
       " ('british', 18),\n",
       " ('diner', 18),\n",
       " ('basketball', 18),\n",
       " ('photography', 18),\n",
       " ('amusement park', 18),\n",
       " ('nanny', 18),\n",
       " ('mouse', 18),\n",
       " ('jazz', 17),\n",
       " ('mockumentary', 17),\n",
       " ('truck', 17),\n",
       " ('farm', 17),\n",
       " ('homophobia', 17),\n",
       " ('bible', 17),\n",
       " ('prostitution', 17),\n",
       " ('new orleans', 17),\n",
       " ('manhattan, new york city', 17),\n",
       " ('aids', 17),\n",
       " ('partner', 17),\n",
       " ('car accident', 17),\n",
       " ('hip-hop', 17),\n",
       " ('horror', 17),\n",
       " ('singing', 17),\n",
       " ('house', 17),\n",
       " ('con man', 17),\n",
       " ('drug traffic', 17),\n",
       " ('silent film', 17),\n",
       " ('agent', 17),\n",
       " ('stripper', 17),\n",
       " ('planned murder', 17),\n",
       " ('afterlife', 17),\n",
       " ('dancer', 17),\n",
       " ('civil war', 17),\n",
       " ('bank robber', 17),\n",
       " ('drama', 17),\n",
       " ('newspaper', 17),\n",
       " ('illness', 17),\n",
       " ('ambush', 17),\n",
       " ('hotel room', 17),\n",
       " ('alien life-form', 17),\n",
       " ('ladykiller', 17),\n",
       " ('virus', 17),\n",
       " ('bullying', 17),\n",
       " ('african american', 17),\n",
       " ('moon', 17),\n",
       " ('fang vamp', 17),\n",
       " ('lover (female)', 17),\n",
       " ('art', 17),\n",
       " ('american', 17),\n",
       " ('sadness', 17),\n",
       " ('anti hero', 17),\n",
       " ('bite', 17),\n",
       " ('parody', 17),\n",
       " ('nerd', 16),\n",
       " ('lake', 16),\n",
       " ('upper class', 16),\n",
       " ('girlfriend', 16),\n",
       " ('bounty hunter', 16),\n",
       " ('british secret service', 16),\n",
       " ('angel', 16),\n",
       " ('revolution', 16),\n",
       " ('jewish', 16),\n",
       " ('wife', 16),\n",
       " ('bride', 16),\n",
       " ('identity', 16),\n",
       " ('cemetery', 16),\n",
       " ('wolf', 16),\n",
       " ('animal attack', 16),\n",
       " ('painter', 16),\n",
       " ('brothel', 16),\n",
       " ('shark', 16),\n",
       " ('twins', 16),\n",
       " ('dating', 16),\n",
       " ('technology', 16),\n",
       " ('ex-cop', 16),\n",
       " ('vietnam veteran', 16),\n",
       " ('vietnam', 16),\n",
       " ('telekinesis', 16),\n",
       " ('video game', 16),\n",
       " ('u.s. army', 16),\n",
       " ('poison', 16),\n",
       " ('mutation', 16),\n",
       " ('mental illness', 16),\n",
       " ('love at first sight', 16),\n",
       " ('immigrant', 16),\n",
       " ('illegal prostitution', 16),\n",
       " ('redemption', 16),\n",
       " ('loss of family', 16),\n",
       " ('santa claus', 16),\n",
       " ('cover-up', 16),\n",
       " ('nasa', 16),\n",
       " ('white house', 16),\n",
       " ('drug lord', 16),\n",
       " ('wealth', 16),\n",
       " ('dirty cop', 16),\n",
       " ('success', 16),\n",
       " ('u.s. navy', 16),\n",
       " ('exorcism', 16),\n",
       " ('kingdom', 16),\n",
       " ('culture clash', 16),\n",
       " ('unemployment', 16),\n",
       " ('foot chase', 16),\n",
       " ('shotgun', 16),\n",
       " ('drug abuse', 16),\n",
       " ('sexual abuse', 16),\n",
       " ('madrid', 16),\n",
       " ('emperor', 16),\n",
       " ('letter', 16),\n",
       " ('new identity', 16),\n",
       " ('prisoners of war', 15),\n",
       " ('christianity', 15),\n",
       " ('smoking', 15),\n",
       " ('human experimentation', 15),\n",
       " ('museum', 15),\n",
       " ('delusion', 15),\n",
       " ('elves', 15),\n",
       " ('highway', 15),\n",
       " ('bridge', 15),\n",
       " ('villain', 15),\n",
       " ('catholicism', 15),\n",
       " ('falsely accused', 15),\n",
       " ('canada', 15),\n",
       " ('president', 15),\n",
       " ('history', 15),\n",
       " ('taxi driver', 15),\n",
       " ('1930s', 15),\n",
       " ('power', 15),\n",
       " ('kgb', 15),\n",
       " ('black magic', 15),\n",
       " ('crush', 15),\n",
       " ('trapped', 15),\n",
       " ('afghanistan', 15),\n",
       " ('mistaken identity', 15),\n",
       " ('fantasy world', 15),\n",
       " ('jew', 15),\n",
       " ('prom', 15),\n",
       " ('duel', 15),\n",
       " ('movie star', 15),\n",
       " ('capitalism', 15),\n",
       " ('unsimulated sex', 15),\n",
       " ('vision', 15),\n",
       " ('religion and supernatural', 15),\n",
       " ('undercover agent', 15),\n",
       " ('russian', 15),\n",
       " ('disguise', 15),\n",
       " ('monk', 15),\n",
       " ('xenophobia', 15),\n",
       " ('burglar', 15),\n",
       " ('shower', 15),\n",
       " ('pornography', 15),\n",
       " ('blindness and impaired vision', 15),\n",
       " ('secret mission', 15),\n",
       " ('buddy cop', 15),\n",
       " ('bravery', 15),\n",
       " ('race against time', 15),\n",
       " ('loyalty', 15),\n",
       " ('firemen', 15),\n",
       " ('japanese', 15),\n",
       " ('philosophy', 15),\n",
       " ('television', 14),\n",
       " ('transporter', 14),\n",
       " ('bodyguard', 14),\n",
       " ('sabotage', 14),\n",
       " ('single father', 14),\n",
       " ('cell phone', 14),\n",
       " ('ensemble cast', 14),\n",
       " ('dual identity', 14),\n",
       " ('gay relationship', 14),\n",
       " ('circus', 14),\n",
       " ('right and justice', 14),\n",
       " ('psychologist', 14),\n",
       " ('undercover cop', 14),\n",
       " ('uncle', 14),\n",
       " ('date', 14),\n",
       " ('guilt', 14),\n",
       " ('prosecution', 14),\n",
       " ('fashion', 14),\n",
       " ('ninja', 14),\n",
       " ('business', 14),\n",
       " ('ballet', 14),\n",
       " ('university', 14),\n",
       " ('killer robot', 14),\n",
       " ('blood splatter', 14),\n",
       " ('extraterrestrial technology', 14),\n",
       " ('water', 14),\n",
       " ('storm', 14),\n",
       " ('hostility', 14),\n",
       " ('period drama', 14),\n",
       " ('teenage crush', 14),\n",
       " ('comedian', 14),\n",
       " ('gas station', 14),\n",
       " ('rock', 14),\n",
       " ('end of the world', 14),\n",
       " ('mexican standoff', 14),\n",
       " ('loss of son', 14),\n",
       " ('woods', 14),\n",
       " ('rap music', 14),\n",
       " ('demonic possession', 14),\n",
       " ('interracial relationship', 14),\n",
       " ('homicide', 14),\n",
       " ('rain', 14),\n",
       " ('rock star', 14),\n",
       " ('tragedy', 14),\n",
       " ('desperation', 14),\n",
       " ('muslim', 14),\n",
       " ('boarding school', 14),\n",
       " ('anarchic comedy', 14),\n",
       " ('diary', 14),\n",
       " ('1950s', 14),\n",
       " ('self sacrifice', 14),\n",
       " ('parallel world', 14),\n",
       " ('home invasion', 14),\n",
       " ('police detective', 14),\n",
       " ('marvel cinematic universe', 14),\n",
       " ('honeymoon', 14),\n",
       " ('clone', 14),\n",
       " ('wish', 14),\n",
       " (\"new year's eve\", 14),\n",
       " ('dystopic future', 14),\n",
       " ('older man younger woman relationship', 14),\n",
       " ('nun', 14),\n",
       " ('actor', 14),\n",
       " ('slaughter', 14),\n",
       " ('mystery', 13),\n",
       " ('general', 13),\n",
       " ('psychology', 13),\n",
       " ('supermarket', 13),\n",
       " ('ancient rome', 13),\n",
       " ('thanksgiving', 13),\n",
       " ('liar', 13),\n",
       " ('execution', 13),\n",
       " ('voodoo', 13),\n",
       " ('underworld', 13),\n",
       " ('schizophrenia', 13),\n",
       " ('hawaii', 13),\n",
       " ('stranded', 13),\n",
       " ('actress', 13),\n",
       " ('jail', 13),\n",
       " ('gunslinger', 13),\n",
       " ('nature', 13),\n",
       " ('fraud', 13),\n",
       " ('convict', 13),\n",
       " ('ex-girlfriend', 13),\n",
       " ('war crimes', 13),\n",
       " ('family holiday', 13),\n",
       " ('dracula', 13),\n",
       " ('man vs machine', 13),\n",
       " ('wretch', 13),\n",
       " ('cabin', 13),\n",
       " ('stalking', 13),\n",
       " ('orphanage', 13),\n",
       " ('teen comedy', 13),\n",
       " ('stop motion', 13),\n",
       " ('missing person', 13),\n",
       " ('secret organization', 13),\n",
       " ('rome', 13),\n",
       " ('combat', 13),\n",
       " ('punk', 13),\n",
       " ('loss of brother', 13),\n",
       " ('tiger', 13),\n",
       " ('trainer', 13),\n",
       " ('miami', 13),\n",
       " ('illegal drugs', 13),\n",
       " ('screenwriter', 13),\n",
       " ('hand to hand combat', 13),\n",
       " ('secret service', 13),\n",
       " ('venice', 13),\n",
       " ('karate', 13),\n",
       " ('loss of child', 13),\n",
       " ('french', 13),\n",
       " ('celebrity', 13),\n",
       " ('mermaid', 13),\n",
       " ('ambition', 13),\n",
       " ('olympic games', 13),\n",
       " ('poetry', 13),\n",
       " ('patriotism', 13),\n",
       " ('factory', 13),\n",
       " ('mythology', 13),\n",
       " ('bully', 13),\n",
       " ('relationship problems', 13),\n",
       " ('graduation', 13),\n",
       " ('cuba', 13),\n",
       " ('masturbation', 13),\n",
       " ('toy comes to life', 13),\n",
       " ('policeman', 13),\n",
       " ('soccer', 13),\n",
       " ('drinking', 13),\n",
       " ('ransom', 13),\n",
       " ('arranged marriage', 13),\n",
       " ('moscow', 13),\n",
       " ('victim', 13),\n",
       " ('ancient world', 13),\n",
       " ('vandalism', 13),\n",
       " ('tv show', 13),\n",
       " ('spacecraft', 13),\n",
       " ('espionage', 13),\n",
       " ('autism', 13),\n",
       " ('iraq', 12),\n",
       " ('boxing match', 12),\n",
       " ('coming out', 12),\n",
       " ('anthology', 12),\n",
       " ('toy', 12),\n",
       " ('unlikely friendship', 12),\n",
       " ('loss', 12),\n",
       " ('kids and family', 12),\n",
       " ('car journey', 12),\n",
       " ('brazilian', 12),\n",
       " ('arrest', 12),\n",
       " ('suspicion', 12),\n",
       " ('transvestism', 12),\n",
       " ('chainsaw', 12),\n",
       " ('ghetto', 12),\n",
       " ('death penalty', 12),\n",
       " ('false identity', 12),\n",
       " ('trial', 12),\n",
       " ('wizard', 12),\n",
       " ('unrequited love', 12),\n",
       " ('architect', 12),\n",
       " ('interview', 12),\n",
       " ('bus', 12),\n",
       " ('girl', 12),\n",
       " ('miracle', 12),\n",
       " ('psychic', 12),\n",
       " ('alaska', 12),\n",
       " ('hypnosis', 12),\n",
       " ('new jersey', 12),\n",
       " ('van', 12),\n",
       " ('tourist', 12),\n",
       " ('massacre', 12),\n",
       " ('lover', 12),\n",
       " ('femme fatale', 12),\n",
       " ('adolescence', 12),\n",
       " ('journey', 12),\n",
       " ('beautiful woman', 12),\n",
       " ('camping', 12),\n",
       " ('secret intelligence service', 12),\n",
       " ('pregnancy and birth', 12),\n",
       " ('accident', 12),\n",
       " ('smuggling', 12),\n",
       " ('underwater', 12),\n",
       " ('one night', 12),\n",
       " ('royalty', 12),\n",
       " ('scuba diving', 12),\n",
       " ('great depression', 12),\n",
       " ('widower', 12),\n",
       " ('education', 12),\n",
       " ('bachelor', 12),\n",
       " ('fame', 12),\n",
       " ('gangster boss', 12),\n",
       " ('film producer', 12),\n",
       " ('innocence', 12),\n",
       " ('human animal relationship', 12),\n",
       " ('company', 12),\n",
       " ('retirement', 12),\n",
       " ('ice hockey', 12),\n",
       " ('knife', 12),\n",
       " ('son', 12),\n",
       " ('beer', 12),\n",
       " ('laboratory', 12),\n",
       " ('disfigurement', 12),\n",
       " ('fish out of water', 12),\n",
       " ('vatican', 12),\n",
       " ('security guard', 12),\n",
       " ('detroit', 12),\n",
       " ('drag queen', 12),\n",
       " ('disabled', 12),\n",
       " ('thailand', 12),\n",
       " ('cigarette smoking', 12),\n",
       " ('earthquake', 12),\n",
       " ('cult', 12),\n",
       " ('twin brother', 12),\n",
       " ('mutiny', 12),\n",
       " ('swordplay', 12),\n",
       " ('shooting', 12),\n",
       " ('haunting', 12),\n",
       " ('film making', 12),\n",
       " ('spain', 12),\n",
       " ('lust', 12),\n",
       " ('homeless person', 12),\n",
       " ('fairy', 12),\n",
       " ('repayment', 12),\n",
       " ('rebellion', 12),\n",
       " ('pop star', 12),\n",
       " ('pedophilia', 12),\n",
       " ('cruelty', 12),\n",
       " ('atomic bomb', 12),\n",
       " ('god', 12),\n",
       " ('job', 12),\n",
       " ('vegetarian', 12),\n",
       " ('literature', 12),\n",
       " ('drug smuggle', 12),\n",
       " ('business man', 12),\n",
       " ('cowboy', 12),\n",
       " ('piano', 12),\n",
       " ('forgiveness', 12),\n",
       " ('terminal illness', 12),\n",
       " ('malayalam', 12),\n",
       " ('captain', 12),\n",
       " ('secret society', 12),\n",
       " ('skiing', 11),\n",
       " ('confidence', 11),\n",
       " ('photograph', 11),\n",
       " ('misfit', 11),\n",
       " ('based on graphic novel', 11),\n",
       " ('automobile racing', 11),\n",
       " ('kids', 11),\n",
       " ('jesus christ', 11),\n",
       " ('shakespeare', 11),\n",
       " ('poker', 11),\n",
       " ('devil', 11),\n",
       " ('aging', 11),\n",
       " ('escape from prison', 11),\n",
       " ('hitchhiker', 11),\n",
       " ('maniac', 11),\n",
       " ('key', 11),\n",
       " ('occult', 11),\n",
       " ('gang war', 11),\n",
       " ('war veteran', 11),\n",
       " ('violent husband', 11),\n",
       " ('peasant', 11),\n",
       " ('loss of husband', 11),\n",
       " ('overdose', 11),\n",
       " ('whale', 11),\n",
       " ('fireworks', 11),\n",
       " ('cook', 11),\n",
       " ('search', 11),\n",
       " ('handcuffs', 11),\n",
       " ('explosive', 11),\n",
       " ('samurai', 11),\n",
       " ('heart attack', 11),\n",
       " ('hell', 11),\n",
       " ('wrestling', 11),\n",
       " ('pig', 11),\n",
       " ('snake', 11),\n",
       " ('coffin', 11),\n",
       " ('climbing', 11),\n",
       " ('american dream', 11),\n",
       " ('child hero', 11),\n",
       " ('prayer', 11),\n",
       " ('murderer', 11),\n",
       " ('attack', 11),\n",
       " ('biker', 11),\n",
       " ('witness', 11),\n",
       " ('strong woman', 11),\n",
       " (\"based on children's book\", 11),\n",
       " ('prohibition', 11),\n",
       " ('music band', 11),\n",
       " ('thriller', 11),\n",
       " ('attempted murder', 11),\n",
       " ('addiction', 11),\n",
       " ('lesbian relationship', 11),\n",
       " ('post traumatic stress  disorder', 11),\n",
       " ('disaster movie', 11),\n",
       " ('grieving', 11),\n",
       " ('roman empire', 11),\n",
       " ('film director', 11),\n",
       " ('babysitter', 11),\n",
       " ('adult animation', 11),\n",
       " ('hostage-taking', 11),\n",
       " ('guitar', 11),\n",
       " ('manipulation', 11),\n",
       " ('skeleton', 11),\n",
       " ('darkness', 11),\n",
       " ('hope', 11),\n",
       " ('archeology\\xa0', 11),\n",
       " ('cave', 11),\n",
       " ('tragic love', 11),\n",
       " ('city', 11),\n",
       " ('assault', 11),\n",
       " ('laser', 11),\n",
       " ('madness', 11),\n",
       " ('underground', 11),\n",
       " ('insomnia', 11),\n",
       " ('spectacle', 11),\n",
       " ('dream sequence', 11),\n",
       " ('fighter', 11),\n",
       " ('missile', 11),\n",
       " ('superhuman strength', 11),\n",
       " ('cataclysm', 11),\n",
       " ('psychological thriller', 11),\n",
       " ('childhood memory', 11),\n",
       " ('salesman', 11),\n",
       " ('airplane crash', 11),\n",
       " ('prank', 11),\n",
       " ('germany', 11),\n",
       " ('soviet union', 11),\n",
       " ('american civil war', 11),\n",
       " ('himalaya', 11),\n",
       " ('mayor', 11),\n",
       " ('disaster film', 11),\n",
       " ('viking', 11),\n",
       " ('liberation', 11),\n",
       " ('witness protection', 11),\n",
       " ('life and death', 11),\n",
       " ('group of friends', 11),\n",
       " ('passion', 11),\n",
       " ('little boy', 11),\n",
       " ...]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keys_sort  # 女导演竟然是提及最多的关键词，确实女导演和男导演比少很多很多 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('kidnapping', 95)"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keys_sort[30] # 选择前30个关键词做one-hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in keys_sort[:30]:\n",
    "    data[col[0]] = data['keyword'].map(lambda x: 1 if col[0] in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7398, 196)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 删去无用列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "budget int64\n",
      "imdb_id object\n",
      "original_title object\n",
      "popularity float64\n",
      "production_companies object\n",
      "production_countries object\n",
      "runtime float64\n",
      "spoken_languages object\n",
      "Keywords object\n",
      "cast object\n",
      "crew object\n",
      "revenue float64\n",
      "collection int64\n",
      "has_homepage int64\n",
      "has_tagline int64\n",
      "has_overview int64\n",
      "year int64\n",
      "month int64\n",
      "day int64\n",
      "spokens int64\n",
      "en int64\n",
      "fr int64\n",
      "hi int64\n",
      "ru int64\n",
      "es int64\n",
      "ja int64\n",
      "it int64\n",
      "ko int64\n",
      "de int64\n",
      "zh int64\n",
      "cn int64\n",
      "ta int64\n",
      "sv int64\n",
      "da int64\n",
      "pt int64\n",
      "ml int64\n",
      "nl int64\n",
      "num_genres int64\n",
      "Comedy int64\n",
      "Drama int64\n",
      "Family int64\n",
      "Romance int64\n",
      "Thriller int64\n",
      "Action int64\n",
      "Animation int64\n",
      "Adventure int64\n",
      "Horror int64\n",
      "Documentary int64\n",
      "Music int64\n",
      "Crime int64\n",
      "Science Fiction int64\n",
      "Mystery int64\n",
      "Foreign int64\n",
      "Fantasy int64\n",
      "War int64\n",
      "Western int64\n",
      "History int64\n",
      "TV Movie int64\n",
      "cast_info object\n",
      "cast_num int64\n",
      "Samuel L. Jackson int64\n",
      "Robert De Niro int64\n",
      "Bruce Willis int64\n",
      "Morgan Freeman int64\n",
      "Liam Neeson int64\n",
      "Willem Dafoe int64\n",
      "Steve Buscemi int64\n",
      "Sylvester Stallone int64\n",
      "Nicolas Cage int64\n",
      "Matt Damon int64\n",
      "J.K. Simmons int64\n",
      "John Goodman int64\n",
      "Julianne Moore int64\n",
      "Christopher Walken int64\n",
      "Robin Williams int64\n",
      "Johnny Depp int64\n",
      "Stanley Tucci int64\n",
      "Harrison Ford int64\n",
      "Richard Jenkins int64\n",
      "Ben Stiller int64\n",
      "Susan Sarandon int64\n",
      "Brad Pitt int64\n",
      "Tom Hanks int64\n",
      "Keith David int64\n",
      "John Leguizamo int64\n",
      "Woody Harrelson int64\n",
      "Bill Murray int64\n",
      "Dennis Quaid int64\n",
      "James Franco int64\n",
      "Dustin Hoffman int64\n",
      "gender_0 int64\n",
      "gender_1 int64\n",
      "gender_2 int64\n",
      "crew_info object\n",
      "crew_gender0 int64\n",
      "crew_gender1 int64\n",
      "crew_gender2 int64\n",
      "Sound int64\n",
      "Lighting int64\n",
      "Art int64\n",
      "Visual Effects int64\n",
      "gender object\n",
      "Directing int64\n",
      "Costume & Make-Up int64\n",
      "Crew int64\n",
      "Actors int64\n",
      "Production int64\n",
      "Camera int64\n",
      "Editing int64\n",
      "Writing int64\n",
      "company object\n",
      "country object\n",
      "Warner Bros. int64\n",
      "Universal Pictures int64\n",
      "Paramount Pictures int64\n",
      "Twentieth Century Fox Film Corporation int64\n",
      "Columbia Pictures int64\n",
      "Metro-Goldwyn-Mayer (MGM) int64\n",
      "New Line Cinema int64\n",
      "Touchstone Pictures int64\n",
      "Walt Disney Pictures int64\n",
      "Columbia Pictures Corporation int64\n",
      "Canal+ int64\n",
      "TriStar Pictures int64\n",
      "Relativity Media int64\n",
      "United Artists int64\n",
      "Miramax Films int64\n",
      "Village Roadshow Pictures int64\n",
      "Regency Enterprises int64\n",
      "DreamWorks SKG int64\n",
      "Fox Searchlight Pictures int64\n",
      "Amblin Entertainment int64\n",
      "Lionsgate int64\n",
      "StudioCanal int64\n",
      "Working Title Films int64\n",
      "Dune Entertainment int64\n",
      "Summit Entertainment int64\n",
      "Dimension Films int64\n",
      "BBC Films int64\n",
      "Orion Pictures int64\n",
      "Hollywood Pictures int64\n",
      "Fox 2000 Pictures int64\n",
      "United States of America int64\n",
      "United Kingdom int64\n",
      "France int64\n",
      "Germany int64\n",
      "Canada int64\n",
      "India int64\n",
      "Italy int64\n",
      "Japan int64\n",
      "Australia int64\n",
      "Spain int64\n",
      "Russia int64\n",
      "China int64\n",
      "Hong Kong int64\n",
      "Belgium int64\n",
      "Ireland int64\n",
      "South Korea int64\n",
      "Sweden int64\n",
      "Mexico int64\n",
      "Netherlands int64\n",
      "Denmark int64\n",
      "country_num int64\n",
      "company_num int64\n",
      "keyword object\n",
      "keyword_num int64\n",
      "woman director int64\n",
      "independent film int64\n",
      "duringcreditsstinger int64\n",
      "based on novel int64\n",
      "murder int64\n",
      "violence int64\n",
      "love int64\n",
      "revenge int64\n",
      "sex int64\n",
      "aftercreditsstinger int64\n",
      "biography int64\n",
      "sport int64\n",
      "friendship int64\n",
      "dystopia int64\n",
      "police int64\n",
      "suspense int64\n",
      "sequel int64\n",
      "nudity int64\n",
      "musical int64\n",
      "teenager int64\n",
      "female nudity int64\n",
      "drug int64\n",
      "los angeles int64\n",
      "new york int64\n",
      "prison int64\n",
      "3d int64\n",
      "high school int64\n",
      "family int64\n",
      "alien int64\n",
      "world war ii int64\n"
     ]
    }
   ],
   "source": [
    "for c in data.columns:\n",
    "    print(c, data[c].dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删去无用或已做处理的列\n",
    "drop_cols = [ col for col in data.columns if data[col].dtype=='object']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['imdb_id',\n",
       " 'original_title',\n",
       " 'production_companies',\n",
       " 'production_countries',\n",
       " 'spoken_languages',\n",
       " 'Keywords',\n",
       " 'cast',\n",
       " 'crew',\n",
       " 'cast_info',\n",
       " 'crew_info',\n",
       " 'gender',\n",
       " 'company',\n",
       " 'country',\n",
       " 'keyword']"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "revenue                 4398\n",
       "Keywords                 669\n",
       "production_companies     414\n",
       "production_countries     157\n",
       "crew                      38\n",
       "cast                      26\n",
       "dtype: int64"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_count(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(drop_cols, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "revenue    4398\n",
       "dtype: int64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_count(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 异常值处理 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>budget</th>\n",
       "      <th>popularity</th>\n",
       "      <th>runtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.398000e+03</td>\n",
       "      <td>7398.000000</td>\n",
       "      <td>7398.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.260146e+07</td>\n",
       "      <td>8.514968</td>\n",
       "      <td>107.717262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.694867e+07</td>\n",
       "      <td>12.165794</td>\n",
       "      <td>21.471326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.933124</td>\n",
       "      <td>94.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.500000e+06</td>\n",
       "      <td>7.435844</td>\n",
       "      <td>104.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.800000e+07</td>\n",
       "      <td>10.920002</td>\n",
       "      <td>118.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.800000e+08</td>\n",
       "      <td>547.488298</td>\n",
       "      <td>338.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             budget   popularity      runtime\n",
       "count  7.398000e+03  7398.000000  7398.000000\n",
       "mean   2.260146e+07     8.514968   107.717262\n",
       "std    3.694867e+07    12.165794    21.471326\n",
       "min    0.000000e+00     0.000001     0.000000\n",
       "25%    0.000000e+00     3.933124    94.000000\n",
       "50%    7.500000e+06     7.435844   104.000000\n",
       "75%    2.800000e+07    10.920002   118.000000\n",
       "max    3.800000e+08   547.488298   338.000000"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['budget','popularity','runtime']\n",
    "data[cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in cols:\n",
    "    sns.distplot(data[col])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in cols:\n",
    "    plt.scatter(data.index,data[col])\n",
    "    plt.title(col)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>budget</th>\n",
       "      <th>popularity</th>\n",
       "      <th>runtime</th>\n",
       "      <th>revenue</th>\n",
       "      <th>collection</th>\n",
       "      <th>has_homepage</th>\n",
       "      <th>has_tagline</th>\n",
       "      <th>has_overview</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>...</th>\n",
       "      <th>female nudity</th>\n",
       "      <th>drug</th>\n",
       "      <th>los angeles</th>\n",
       "      <th>new york</th>\n",
       "      <th>prison</th>\n",
       "      <th>3d</th>\n",
       "      <th>high school</th>\n",
       "      <th>family</th>\n",
       "      <th>alien</th>\n",
       "      <th>world war ii</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>6843500</td>\n",
       "      <td>3.800073</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10703234.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>0</td>\n",
       "      <td>0.402368</td>\n",
       "      <td>0.0</td>\n",
       "      <td>234748.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>925</th>\n",
       "      <td>0</td>\n",
       "      <td>1.926826</td>\n",
       "      <td>0.0</td>\n",
       "      <td>850259.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>11000000</td>\n",
       "      <td>5.010563</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12935800.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2011</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>0</td>\n",
       "      <td>1.623440</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39598448.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1975</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1542</th>\n",
       "      <td>750000</td>\n",
       "      <td>0.201582</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1875</th>\n",
       "      <td>0</td>\n",
       "      <td>0.229233</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2151</th>\n",
       "      <td>5000000</td>\n",
       "      <td>0.414793</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3919731.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2006</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2499</th>\n",
       "      <td>3500000</td>\n",
       "      <td>0.884241</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2294357.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2646</th>\n",
       "      <td>0</td>\n",
       "      <td>0.504000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76000000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2786</th>\n",
       "      <td>0</td>\n",
       "      <td>0.625099</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2001</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2866</th>\n",
       "      <td>5579750</td>\n",
       "      <td>2.208906</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8927600.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2012</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4074</th>\n",
       "      <td>380000</td>\n",
       "      <td>0.043519</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4222</th>\n",
       "      <td>0</td>\n",
       "      <td>0.001393</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1993</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4431</th>\n",
       "      <td>336029</td>\n",
       "      <td>0.562568</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2008</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5520</th>\n",
       "      <td>2500000</td>\n",
       "      <td>0.209434</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2010</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5845</th>\n",
       "      <td>0</td>\n",
       "      <td>2.144310</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5849</th>\n",
       "      <td>2500000</td>\n",
       "      <td>0.922522</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2005</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6210</th>\n",
       "      <td>3800000</td>\n",
       "      <td>0.072704</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1999</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6804</th>\n",
       "      <td>0</td>\n",
       "      <td>0.003647</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2005</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7321</th>\n",
       "      <td>0</td>\n",
       "      <td>0.191219</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2008</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21 rows × 182 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        budget  popularity  runtime     revenue  collection  has_homepage  \\\n",
       "id                                                                          \n",
       "391    6843500    3.800073      0.0  10703234.0           0             0   \n",
       "592          0    0.402368      0.0    234748.0           0             0   \n",
       "925          0    1.926826      0.0    850259.0           0             0   \n",
       "978   11000000    5.010563      0.0  12935800.0           0             0   \n",
       "1256         0    1.623440      0.0  39598448.0           0             0   \n",
       "1542    750000    0.201582      0.0         3.0           0             0   \n",
       "1875         0    0.229233      0.0         1.0           0             1   \n",
       "2151   5000000    0.414793      0.0   3919731.0           0             0   \n",
       "2499   3500000    0.884241      0.0   2294357.0           1             0   \n",
       "2646         0    0.504000      0.0  76000000.0           0             0   \n",
       "2786         0    0.625099      0.0  10000000.0           0             0   \n",
       "2866   5579750    2.208906      0.0   8927600.0           1             0   \n",
       "4074    380000    0.043519      0.0         NaN           0             1   \n",
       "4222         0    0.001393      0.0         NaN           0             0   \n",
       "4431    336029    0.562568      0.0         NaN           0             0   \n",
       "5520   2500000    0.209434      0.0         NaN           0             0   \n",
       "5845         0    2.144310      0.0         NaN           0             0   \n",
       "5849   2500000    0.922522      0.0         NaN           0             0   \n",
       "6210   3800000    0.072704      0.0         NaN           0             0   \n",
       "6804         0    0.003647      0.0         NaN           0             0   \n",
       "7321         0    0.191219      0.0         NaN           0             0   \n",
       "\n",
       "      has_tagline  has_overview  year  month  ...  female nudity  drug  \\\n",
       "id                                            ...                        \n",
       "391             0             0  2012     12  ...              0     0   \n",
       "592             0             0  2003     12  ...              0     0   \n",
       "925             0             1  2013     11  ...              0     0   \n",
       "978             0             0  2011     10  ...              0     0   \n",
       "1256            0             1  1975      8  ...              0     0   \n",
       "1542            0             0  2014      6  ...              0     0   \n",
       "1875            0             1  2007      1  ...              0     0   \n",
       "2151            0             0  2006     10  ...              0     0   \n",
       "2499            0             1  2010      4  ...              0     0   \n",
       "2646            0             1  2014      4  ...              0     0   \n",
       "2786            0             1  2001      4  ...              0     0   \n",
       "2866            0             0  2012     11  ...              0     0   \n",
       "4074            0             1  2010      1  ...              0     0   \n",
       "4222            0             1  1993      3  ...              0     0   \n",
       "4431            0             0  2008      8  ...              0     0   \n",
       "5520            0             0  2010      5  ...              0     0   \n",
       "5845            0             0  2015      1  ...              0     0   \n",
       "5849            0             1  2005      2  ...              0     0   \n",
       "6210            0             0  1999      3  ...              0     0   \n",
       "6804            0             1  2005      2  ...              0     0   \n",
       "7321            0             0  2008     11  ...              0     1   \n",
       "\n",
       "      los angeles  new york  prison  3d  high school  family  alien  \\\n",
       "id                                                                    \n",
       "391             0         0       0   0            0       0      0   \n",
       "592             0         0       0   0            0       0      0   \n",
       "925             0         0       0   0            0       0      0   \n",
       "978             0         0       0   0            0       0      0   \n",
       "1256            0         0       0   0            0       0      0   \n",
       "1542            0         0       0   0            0       0      0   \n",
       "1875            0         0       0   0            0       0      0   \n",
       "2151            0         0       0   0            0       0      0   \n",
       "2499            0         0       0   0            0       0      0   \n",
       "2646            0         0       0   0            0       0      0   \n",
       "2786            0         0       0   0            0       0      0   \n",
       "2866            0         0       0   0            0       0      0   \n",
       "4074            0         0       0   0            0       0      0   \n",
       "4222            0         0       0   0            0       0      0   \n",
       "4431            0         0       0   0            0       0      0   \n",
       "5520            0         0       0   0            0       0      0   \n",
       "5845            0         0       0   0            0       0      0   \n",
       "5849            0         0       0   0            0       0      0   \n",
       "6210            0         0       0   0            0       0      0   \n",
       "6804            0         0       0   0            0       0      0   \n",
       "7321            0         0       0   0            0       0      0   \n",
       "\n",
       "      world war ii  \n",
       "id                  \n",
       "391              0  \n",
       "592              0  \n",
       "925              0  \n",
       "978              0  \n",
       "1256             0  \n",
       "1542             0  \n",
       "1875             0  \n",
       "2151             0  \n",
       "2499             0  \n",
       "2646             0  \n",
       "2786             0  \n",
       "2866             0  \n",
       "4074             0  \n",
       "4222             0  \n",
       "4431             0  \n",
       "5520             0  \n",
       "5845             0  \n",
       "5849             0  \n",
       "6210             0  \n",
       "6804             0  \n",
       "7321             0  \n",
       "\n",
       "[21 rows x 182 columns]"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['runtime']<1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "# runtime 为0的用众数替换\n",
    "data['runtime'].replace(0.0, data1['runtime'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data['runtime']<1).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = data[data['revenue'].notnull()]\n",
    "test = data[data['revenue'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = train.drop('revenue', axis=1)\n",
    "y = train['revenue']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size=0.3, random_state=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(y, shade=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y的偏度值为4.542285301660683,峰度值为27.78254883576798\n",
      "log(y)的偏度值为-1.6793767052948028,峰度值为4.3710740703449\n"
     ]
    }
   ],
   "source": [
    "print(\"y的偏度值为{0},峰度值为{1}\".format(y.skew(), y.kurt()))\n",
    "print(\"log(y)的偏度值为{0},峰度值为{1}\".format(np.log(y).skew(), np.log(y).kurt()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(np.log(y),shade=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.286794719055586"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_log_error\n",
    "model = XGBRegressor()\n",
    "model.fit(x_train, np.log(y_train))\n",
    "predt = model.predict(x_test)\n",
    "mean_squared_log_error(np.exp(predt),y_test, squared=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.3149307114588273"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "lass = Lasso(alpha=0.01, max_iter=3000)\n",
    "lass.fit(x_train, np.log(y_train))\n",
    "predt = lass.predict(x_test)\n",
    "mean_squared_log_error(np.exp(predt),y_test, squared=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.1762154975510506"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestRegressor()\n",
    "rf.fit(x_train, np.log(y_train))\n",
    "predt = rf.predict(x_test)\n",
    "mean_squared_log_error(np.exp(predt),y_test, squared=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.1299076166009665"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdbt = GradientBoostingRegressor()\n",
    "gdbt.fit(x_train, np.log(y_train))\n",
    "predt = gdbt.predict(x_test)\n",
    "mean_squared_log_error(np.exp(predt), y_test, squared=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test.drop(\"revenue\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "class averagemodel:\n",
    "    def __init__(self, models):\n",
    "        self.models = models\n",
    "        \n",
    "    def fit(self,x,y):\n",
    "        for model in self.models:\n",
    "            model.fit(x,y)\n",
    "            \n",
    "    def predict(self, x):\n",
    "        prediction = np.column_stack([ model.predict(x) for model in self.models])\n",
    "        return prediction.mean(axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "average = averagemodel([model, lass,rf,gdbt])\n",
    "def saveans(pred):\n",
    "    df = pd.DataFrame({'id':test.index, 'revenue':pred})\n",
    "    df.to_csv(\"./TMDB/sumbisson.csv\", index=False)\n",
    "pred = average.predict(test)\n",
    "saveans(np.exp(pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 最终结果2.10445"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
